Index

A

• A-Priori algorithm
  • about / A-Priori algorithms
  • input data characteristics / Input data characteristics and data structure
  • data structure / Input data characteristics and data structure
  • join action / The A-Priori algorithm
  • prune action / The A-Priori algorithm
  • R implementation / The R implementation
  • variants / A-Priori algorithm variants
• abline() function
  • used, for adding marker lines / Adding marker lines at specific x and y values using abline, How it works..., There's more...
  • used, for adding vertical markers / Adding vertical markers to indicate specific time events, How it works..., There's more...
  / How it works...
• absolute transaction costs
  • optimal hedging / Optimal hedging in the case of absolute transaction costs
• abstraction / Abstraction
• activation function / From biological to artificial neurons
  • about / Activation functions
  • threshold activation function / Activation functions
  • unit step activation function / Activation functions
  • sigmoid activation function / Activation functions
• AdaBoost
  • about / Boosting
• AdaBoost.M1 algorithm / Boosting
• AdaBoost algorithm
  • about / The boosting and AdaBoost algorithms
• adaptive boosting
  • about / Boosting the accuracy of decision trees, Boosting
• Advanced Measurement Approach (AMA) / Minimum capital requirements
• affinity propagation (AP) clustering
  • about / Unsupervised image categorization and affinity propagation clustering
  • R implementation / The R implementation
  • used, for unsupervised image categorization / Unsupervised image categorization
  • spectral clustering algorithm / The spectral clustering algorithm
• agglomerative clustering
  • about / News categorization and hierarchical clustering
  • pseudocode / Agglomerative hierarchical clustering
• aggregate() function
  • about / How it works...
• algorithm, for association rule generation
  • R implementation / The R implementation
• allocation function / Understanding ensembles
• alpha level (α level) / Null Hypothesis Significance Testing
• Alternative Standard Approach (ASTA) / Minimum capital requirements
• am / Introduction
• Analysis of Covariance (ANCOVA) / Logistic regression
• Analysis of Variance (ANOVA)
  • about / Testing more than two means
  • assumptions / Assumptions of ANOVA
• analytical VaR / Analytical VaR
• annotate() function / How it works...
• annotations
  • fonts, setting / Setting fonts for annotations and titles, How it works..., See also
• anonymous functions / Functions
• Apache Hadoop
  • about / Parallel cloud computing with MapReduce and Hadoop
• application, in R
  • about / Application in R
• Application Programming Interfaces (APIs)
  • about / Parsing JSON from web APIs
• Apriori
  • property / The Apriori algorithm for association rule learning
• Apriori algorithm
  • for association rule learning / The Apriori algorithm for association rule learning
  • strengths / The Apriori algorithm for association rule learning
• Apriori principle
  • used, for building set of rules / Building a set of rules with the Apriori principle
• APT
  • about / Arbitrage pricing theory
  • assumptions / Arbitrage pricing theory
  • implementing / Implementation of APT
  • Fama-French three-factor model / Fama-French three-factor model
  • estimating, with principal component analysis / Estimation of APT with principal component analysis
• AR(1) model
  • estimating / AR(1) estimation and forecasting
  • forecasting / AR(1) estimation and forecasting
• arguments / Arithmetic and assignment
• arithmetic operators / Arithmetic and assignment
• arrows() function / How it works..., How it works...
• Artificial Neural Network (ANN)
  • about / Understanding neural networks
• as.Date() function / How it works..., How it works...
• as.matrix() function / How it works...
• Asian options
  • about / A glance beyond vanillas
• assignment operators / Arithmetic and assignment
• association rules
  • about / Association rules, Understanding association rules
  • generating, with algorithm / The algorithm to generate association rules
  • potential applications / Understanding association rules
  • rule interest, measuring / Measuring rule interest – support and confidence
  • set of rules, building with Apriori principle / Building a set of rules with the Apriori principle
  • frequently purchased groceries, identifying with / Example – identifying frequently purchased groceries with association rules
• associations
  • about / An overview of associations and patterns
• associative classification
  • about / The associative classification
  • Classification Based on Association (CBA) / The associative classification
  • Classification Based on Multiple Association Rules (CMAR) / The associative classification
• ATM (at-the-money) / Dynamic delta hedge
• attribute
  • about / Data attributes and description
• automated parameter tuning
  • caret package used for / Using caret for automated parameter tuning
  • requisites / Using caret for automated parameter tuning
• Auto Regressive Integrated Moving Average (ARIMA) algorithm
  • about / Predicting future prices and time-series analysis, The ARIMA algorithm
  • future prices, predicting / Predicting future prices
• axes, histograms
  • adjusting / Adjusting histogram styles – bar colors, borders, and axes, How it works..., There's more
• axis() command / How it works..., How it works...
• axis annotations
  • about / Setting colors for text elements – axis annotations, labels, plot titles, and legends
  • colors, setting / How to do it..., How it works..., There's more...
  • adjusting / Adjusting axis annotations and tick marks, How to do it..., How it works..., There's more...
• axis labels
  • about / Setting colors for text elements – axis annotations, labels, plot titles, and legends
  • colors, setting / How it works..., There's more...
• axon
  • about / From biological to artificial neurons

B

• backpropagation
  • neural networks, training with / Training neural networks with backpropagation
  • about / Training neural networks with backpropagation
• backtesting
  • about / Backtesting
• bag-of-words / Step 2 – exploring and preparing the data
• bagged trees technique / Random forests
• bagging / Random forests
  • about / Bagging
• bagging algorithm
  • about / The bagging algorithm
  • input parameters / The bagging algorithm
• bandwidth / Probability distributions
• bank loans example, with C5.0 decision trees
  • data, collecting / Step 1 – collecting data
  • data, exploring / Step 2 – exploring and preparing the data
  • data, preparing / Step 2 – exploring and preparing the data
  • random training, creating / Data preparation – creating random training and test datasets
  • test datasets, creating / Data preparation – creating random training and test datasets
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
• barchart() command / How it works…
• bar charts
  • creating / Creating bar charts, How to do it..., Creating bar charts, How it works…, There's more…, See also
  • working / How it works..., There's more...
  • creating, with multiple factor variable / Creating bar charts with more than one factor variable, How it works...
  • creating, with horizontal bar orientation / Adjusting the orientation of bars – horizontal and vertical, How it works..., There's more...
  • creating, with vertical bar orientation / Adjusting the orientation of bars – horizontal and vertical, How it works..., There's more...
  • widths, adjusting / Adjusting bar widths, spacing, colors, and borders, Getting ready, How it works...
  • spacing, adjusting / Adjusting bar widths, spacing, colors, and borders, Getting ready, How it works...
  • colors, adjusting / Adjusting bar widths, spacing, colors, and borders, Getting ready, How it works...
  • borders, adjusting / Adjusting bar widths, spacing, colors, and borders, Getting ready, How it works...
  • values, displaying / Displaying values on top of or next to the bars, How to do it..., How it works..., See also
  • labels, placing inside bars / Placing labels inside bars, How to do it..., How it works...
  • creating, with vertical error bars / Creating bar charts with vertical error bars, How to do it..., How it works..., There's more...
  • stacked bar charts, creating / Creating stacked bar charts, How it works…, There's more…
  • creating, to visualize cross-tabulation / Creating bar charts to visualize cross-tabulation, How it works…, There's more…
  • creating, ggplot2 library used / Creating bar charts, Getting ready, How it works…, See also
  • creating, with error bars / Creating a bar chart with error bars, Getting ready, How it works…, There's more…
• bar colors, histograms
  • adjusting / Adjusting histogram styles – bar colors, borders, and axes, There's more
• barplot() function / How to do it..., How it works...
• barplot function / Base graphics using the default package
• bars
  • colors, setting / Getting ready, How to do it..., There's more...
• base graphics
  • using, default package / Base graphics using the default package
• Basel Accords
  • principles / Principles of the Basel Accords
  • Basel I / Basel I
  • Basel II / Basel II
  • Basel III / Basel III
• Basel I / Basel I
• Basel II
  • about / Basel II
  • objectives / Basel II
  • minimum capital requirements / Minimum capital requirements
  • supervisory review / Supervisory review
  • transparency / Transparency
• Basel III / Basel III
• Basel Regulatory Framework
  • URL / Principles of the Basel Accords
• base R / Visualization methods
• Basic Indicator Approach (BIA) / Minimum capital requirements
• basket
  • about / The frequent itemset
• batch mode / Navigating the basics
• Bayes classification
  • about / Trojan traffic identification method and Bayes classification
  • used, for Trojan traffic identification / Trojan traffic identification method and Bayes classification, Trojan traffic identification method
  • prior probability estimation / Prior probability estimation
  • likelihood estimation / Likelihood estimation
  • pseudocode / The Bayes classification
  • R implementation / The R implementation
• Bayes factors / The Bayesian independent samples t-test
• Bayesian hierarchical clustering algorithm
  • about / The Bayesian hierarchical clustering algorithm
  • pseudocode / The Bayesian hierarchical clustering algorithm
• Bayesian independent samples t-test
  • performing / The Bayesian independent samples t-test
• Bayesian linear regression / Advanced topics
• Bayesian methods
  • basic concepts / Basic concepts of Bayesian methods
• Bayesian methods, basics concepts
  • probability / Understanding probability
• Bayesian methods,basics concepts
  • joint probability / Understanding joint probability
  • conditional probability / Computing conditional probability with Bayes' theorem
• BBN algorithm
  • about / Biological traits and the Bayesian belief network, The Bayesian belief network (BBN) algorithm
  • R implementation / The R implementation
  • biological traits / Biological traits
• bell curve / Central tendency
• Beowulf cluster
  • about / Working in parallel with multicore and snow
• beta level (β level) / When things go wrong
• betweenness centrality
  • about / Analyzing and visualizing network data
• bias / The case of linearly separable data
• bias-variance trade-off
  • about / The bias-variance trade-off
  • cross-validation / Cross-validation
  • balance, striking / Striking a balance
• bias-variance tradeoff / Choosing an appropriate k
• big data
  • about / Big data
  • data types / Big data
  • scalability / Scalability and efficiency
  • efficiency / Scalability and efficiency
  • loading / Loading big data
• big data analysis, in R
  • about / Introduction to big data analysis in R
• big data linear regression analysis
  • about / Big data linear regression analysis
  • big data, loading / Loading big data
  • linear regression model, fitting on large datasets / Fitting a linear regression model on large datasets
• biglm package
  • regression models, building / Building bigger regression models with biglm
• big matrices
  • loading / Loading big matrices
• bigmemory package
  • massive matrices, using with / Using massive matrices with bigmemory
  • URL / Using massive matrices with bigmemory
• bigrf package
  • random forests, building / Growing bigger and faster random forests with bigrf
  • URL / Growing bigger and faster random forests with bigrf
• bimodal / Measuring the central tendency – the mode
• Binary options
  • about / A glance beyond vanillas
• binning
  • about / Junk, noisy data, or outlier, Using numeric features with Naive Bayes
• binomial distribution / Null Hypothesis Significance Testing
• bins
  • about / Using numeric features with Naive Bayes
• Bioconductor
  • about / Analyzing bioinformatics data
  • URL / Analyzing bioinformatics data
• bioinformatics
  • about / Analyzing bioinformatics data
• bioinformatics data
  • analyzing / Analyzing bioinformatics data
• BIRCH algorithm
  • about / The BIRCH algorithm
  • CF-Tree rebuilding / The BIRCH algorithm
  • CF-Tree insertion / The BIRCH algorithm
  • pseudocode / The BIRCH algorithm
• Bitcoin prices
  • forecasting / Forecasting bitcoin prices
  • strategy, evaluating / Evaluation of the strategy
• bivariate relationship (two variable) / Multivariate data
• bivariate relationships
  • about / Exploring relationships between variables
• Black-Scholes surface
  • about / How R can help a lot
• Black model
  • about / The Black model
  • cap, pricing with / Pricing a cap with Black's model
• blind tasting experience example / The k-NN algorithm
• blowby / Simple linear regression
• body mass index (BMI) / Step 1 – collecting data
• Bonferroni's Principle
  • about / The limitations of statistics on data mining
• Bonferroni correction / Testing more than two means
  • about / The limitations of statistics on data mining
• boosting
  • about / Boosting
• boosting algorithm
  • about / The boosting and AdaBoost algorithms
• bootstrap aggregating / Random forests
  • about / Bagging
• bootstrap sampling / Bootstrap sampling
• borders, histograms
  • adjusting / Adjusting histogram styles – bar colors, borders, and axes, How it works..., There's more
• box-and-whisker plot / Relationships between a categorical and a continuous variable
• box-and-whiskers plot / Visualizing numeric variables – boxplots
• box plot
  • creating / Creating a boxplot, How it works…, There's more…
• boxplot() command / How to do it...
• boxplot() function / There's more...
• boxplot() method
  • arguments / There's more
• boxplot function / Base graphics using the default package
• box plots
  • creating / Creating box plots
  • working / How it works..., There's more...
  • creating, with narrow boxes / Creating box plots with narrow boxes for a small number of variables, How to do it..., How it works..., There's more
  • grouping, over variable / Grouping over a variable, How it works..., There's more
  • creating, with notches / Creating box plots with notches, How it works..., There's more
  • styling / Changing the box styling, How it works..., There's more
  • whiskers, adjusting / Adjusting the extent of plot whiskers outside the box, How to do it..., How it works...
  • number of observations, displaying / Showing the number of observations, Getting ready, How it works..., There's more
  • variable, splitting at arbitrary intervals / Splitting a variable at arbitrary values into subsets, Getting ready, How it works..., There's more
  • creating, ggplot2 library used / Creating a box plot, How it works…
• box styles
  • selecting / Choosing box styles, How it works..., There's more...
• box widths
  • varying / Varying box widths by the number of observations, How it works...
• BP algorithm
  • about / Classification using the backpropagation algorithm
  • input parameters / The BP algorithm
  • pseudocode / The BP algorithm
  • R implementation / The R implementation
  • parallel version, with MapReduce / Parallel version with MapReduce
• branches
  • about / Understanding decision trees
• breast cancer
  • diagnosing, with k-NN algorithm / Example – diagnosing breast cancer with the k-NN algorithm
• breast cancer example
  • data, collecting / Step 1 – collecting data
  • data, exploring / Step 2 – exploring and preparing the data
  • data, preparing / Step 2 – exploring and preparing the data
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
• brute-force algorithm
  • about / The brute-force algorithm

C

• C4.5 algorithm
  • used, for web spam detection / Web spam detection using C4.5, Web spam detection
  • characteristics / Web spam detection using C4.5
  • pseudocode / The C4.5 algorithm
  • R implementation / The R implementation
  • parallel version, MapReduce / A parallel version with MapReduce
• C5.0 algorithm
  • about / The C5.0 decision tree algorithm
  • split, selecting / Choosing the best split
  • decision tree, pruning / Pruning the decision tree
• calendar.plot() function / There's more
• calendar heat maps
  • time series data, visualizing / Visualizing time series as calendar heat maps, How to do it..., How it works..., There's more
• call quanto
  • pricing formula, used for / Pricing formula for a call quanto
  • pricing, in R / Pricing a call quanto in R
• candle patterns, key reversal
  • about / Candle patterns: key reversal
• cap
  • pricing, with Black model / Pricing a cap with Black's model
• carb / Introduction
• caret package
  • using, for automated parameter tuning / Using caret for automated parameter tuning
  • URL / Using caret for automated parameter tuning, Training and evaluating models in parallel with caret
  • used, for evaluating models in parallel / Training and evaluating models in parallel with caret
• CART algorithm
  • used, for web key resource page judgment / Web key resource page judgment using CART, Web key resource page judgment
  • about / Web key resource page judgment using CART
  • characteristics / Web key resource page judgment using CART
  • pseudocode / The CART algorithm
  • R implementation / The R implementation
• cash-flow
  • preparing / Preparing the cash-flow
• cash-flow generator functions / Cash-flow generator functions
• categorical / Types of input data
• categorical attributes
  • about / Categorical attributes
  • nominal / Categorical attributes
  • ordinal / Categorical attributes
• categorical data
  • multivariate visualization / Multivariate categorical data visualization, How it works…, There's more…
• categorical variable
  • and continuous variable, relationship between / Relationships between a categorical and a continuous variable
  • relationships, describing / Relationships between two categorical variables
  • visualization methods / Categorical and continuous variables
• categorical variables
  • about / Exploring categorical variables
  • central tendency, measuring / Measuring the central tendency – the mode
• cell body / From biological to artificial neurons
• central tendency
  • measuring / Central tendency
• centroid / Using distance to assign and update clusters
• CF-Tree
  • about / The BIRCH algorithm
• chameleon algorithm
  • about / The chameleon algorithm
  • sparsification / The chameleon algorithm
  • graph partitioning / The chameleon algorithm
  • agglomerative hierarchical clustering / The chameleon algorithm
• character data type / Logicals and characters
• characteristics, neural networks
  • activation function / From biological to artificial neurons
  • network topology / From biological to artificial neurons
  • training algorithm / From biological to artificial neurons
• Charm algorithm
  • about / The Charm algorithm with closed frequent itemsets
  • R implementation / The R implementation
• charts, bitcoin
  • plotting / Plotting charts - bitcoin
  • URL / Plotting charts - bitcoin
• chi-square distribution / Testing independence of proportions
• chi-squared statistic / Testing independence of proportions
• circular decision boundary / The circular decision boundary
• CLARA algorithm
  • about / The CLARA algorithm
  • pseudocode / The CLARA algorithm
  • R implementation / The R implementation
• CLARANS algorithm
  • about / CLARANS
  • input parameters / The CLARANS algorithm
  • pseudocode / The CLARANS algorithm
  • R implementation / The R implementation
• classification
  • about / Classification
  • training (supervised learning) / Classification
  • validation / Classification
  / Types of machine learning algorithms
• classification, with frequent patterns
  • about / Classification using frequent patterns
  • associative classification / The associative classification
  • discriminative frequent pattern-based classification / Discriminative frequent pattern-based classification
  • R implementation / The R implementation
  • sentential frequent itemsets / Text classification using sentential frequent itemsets
• classification-based methods
  • about / Monitoring the performance of the web server and classification-based methods
  • OCSVM (One Class SVM) algorithm / The OCSVM algorithm
  • one-class nearest neighbor algorithm / The one-class nearest neighbor algorithm
  • R implementation / The R implementation
  • web server performance, monitoring / Monitoring the performance of the web server
• classification and regression training (caret package) / Beyond accuracy – other measures of performance
• Classification and Regression Tree (CART) algorithm / Understanding regression trees and model trees
• Classification Based on Association (CBA)
  • about / CBA
  • pseudocode / CBA
• Classification Based on Multiple Association Rules (CMAR)
  • about / The associative classification
• classification performance
  • measuring / Measuring performance for classification
• classification prediction data-classification prediction data
  • working with / Working with classification prediction data in R
• classification rules
  • setting / Setting classification rules
  • about / Understanding classification rules
  • separate and conquer / Separate and conquer
  • 1 R algorithm / The 1R algorithm
  • RIPPER algorithm / The RIPPER algorithm
  • obtaining, from decision trees / Rules from decision trees
• classifier
  • selecting / Choosing a classifier
  • vertical decision boundary / The vertical decision boundary
  • diagonal decision boundary / The diagonal decision boundary
  • crescent decision boundary / The crescent decision boundary
  • circular decision boundary / The circular decision boundary
• class imbalance problem / Measuring performance for classification
• CLIQUE algorithm
  • about / Web sentiment analysis and CLIQUE
  • characteristics / Web sentiment analysis and CLIQUE
  • pseudocode / The CLIQUE algorithm
  • R implementation / The R implementation
  • web sentiment analysis / Web sentiment analysis
• closed frequent itemsets
  • mining, with Charm algorithm / The Charm algorithm with closed frequent itemsets
• closely packed data points
  • distinguishing, jitter() function used / Using jitter to distinguish closely packed data points, How to do it..., How it works...
• clustering / Types of machine learning algorithms
  • about / Understanding clustering
  • as machine learning task / Clustering as a machine learning task
• clustering, k-means clustering algorithm
  • about / The k-means clustering algorithm
  • distance, used for assigning cluster / Using distance to assign and update clusters
  • distance, used for updating cluster / Using distance to assign and update clusters
  • appropriate number of clusters, selecting / Choosing the appropriate number of clusters
• clustering-based methods
  • about / Intrusion detection and clustering-based methods
  • hierarchical clustering algorithm / Hierarchical clustering to detect outliers
  • k-means algorithm / The k-means-based algorithm
  • ODIN algorithm / The ODIN algorithm
  • R implementation / The R implementation
• CMYK (Cyan Magenta Yellow Key) color model / There's more
• Cohen’s d / Don't be fooled!
• cointegration / Cointegration
• col argument / How it works...
• collective outliers
  • about / Collective outliers on spatial data
  • route outlier detection (ROD) algorithm / The route outlier detection (ROD) algorithm
  • characteristics / Characteristics of collective outliers
• colorBrewer
  • URL / There's more...
• color combinations
  • selecting / Choosing color combinations and palettes, How it works..., There's more...
• colors
  • of points, setting / Setting colors of points, lines, and bars, How to do it..., How it works...
  • of lines, setting / How to do it..., How it works...
  • of bars, setting / How to do it..., How it works...
  • of axis annotations, setting / How to do it..., There's more...
  • of axis labels, setting / How it works..., There's more...
  • of plot titles, setting / How it works..., There's more...
  • of legends, setting / There's more...
• column-major order / Matrixes and arrays
• combination function / Understanding ensembles
• comments / Arithmetic and assignment
• Complete Unified Device Architecture (CUDA)
  • about / GPU computing
• Comprehensive R Archive Network (CRAN) / Working with packages
  • about / What are the disadvantages of R?, Machine learning with R
  • URL / Machine learning with R
• concrete strength, modeling with ANNs
  • about / Example – Modeling the strength of concrete with ANNs
  • data, collecting / Step 1 – collecting data
  • data, preparing / Step 2 – exploring and preparing the data
  • data, exploring / Step 2 – exploring and preparing the data
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
• conditional anomaly detection (CAD) algorithm
  • about / The conditional anomaly detection (CAD) algorithm
  • R implementation / The R implementation
• conditional histogram
  • creating / Creating a conditional histogram, How to do it…, There's more…
• conditional probability
  • about / Computing conditional probability with Bayes' theorem
• conditional probability tables (CPT)
  • about / Biological traits and the Bayesian belief network
• conditional scatter plot
  • creating / Creating a conditional scatter plot, How to do it…, How it works…
• conditional value at risk (CVaR) / Monte-Carlo simulation
• confidence intervals
  • using / Interval estimation
  • about / How did we get 1.96?
• confusion matrix / Confusion matrices
  • about / A closer look at confusion matrices
  • used, for measuring performance / Using confusion matrices to measure performance
• connections
  • revealing / Revealing connections
• constraint-based frequent pattern mining
  • about / Hybrid association rules mining, Constraint-based frequent pattern mining
• contextual outliers
  • mining / Detecting novelty in text, topic detection, and mining contextual outliers
  • conditional anomaly detection (CAD) algorithm / The conditional anomaly detection (CAD) algorithm
• continuous, numeric attributes
  • about / Numeric attributes
• continuous variable
  • and categorical variable, relationship between / Relationships between a categorical and a continuous variable
  • relationships, describing / The relationship between two continuous variables
  • covariance / Covariance
  • correlation coefficients / Correlation coefficients
  • multiple correlations, comparing / Comparing multiple correlations
• continuous variables
  • visualization methods / Categorical and continuous variables
• contour function / Base graphics using the default package
• contour plots
  • creating / Creating contour plots, How it works..., There's more, See also
• controlled experiment / Testing two means
• control object / Customizing the tuning process
• convex hull / The case of linearly separable data
• core-periphery decomposition
  • about / Core-periphery decomposition
  • implementation, in R / Implementation in R
  • results / Results
• corpus / Data preparation – cleaning and standardizing text data
• correlation
  • about / Correlations
• correlation coefficients / Correlation coefficients
• correlation heat maps
  • creating / Creating correlation heat maps, How it works...
• correlation matrix
  • creating, pairs plot used / Correlation matrix using pairs plots, How it works...
• correlation rules
  • about / Correlation rules
• cost complexity pruning / Decision trees
• countries
  • global data plotting, on world map / Plotting global data by countries on a world map, How it works..., See also
• covariance / Covariance
• covariance matrix / Comparing multiple correlations
• Cox-Ingersoll-Ross model
  • about / The Cox-Ingersoll-Ross model
• CRAN
  • URL / Introduction, Improving the performance of R
  • references / Introduction
  • about / Improving the performance of R
• CRAN task view
  • URL / Analyzing bioinformatics data
• CRAN Web Technologies
  • URL / Working with online data and services
• credit card fraud detection
  • about / Credit card fraud detection
• credit card transaction flow
  • mining / The credit card transaction flow
• credit default swap (CDS) / Credit risk
• credit risk / Credit risk
• crescent decision boundary / The crescent decision boundary
• CRISP-DM
  • about / The data mining process
  • business understanding / CRISP-DM
  • data understanding / CRISP-DM
  • data preparation / CRISP-DM
  • modeling / CRISP-DM
  • evaluation / CRISP-DM
  • deployment / CRISP-DM
• CRM (Customer Relation Management)
  • about / Web click streams
• cross-tabulation / Relationships between two categorical variables
  • visualizing, bar charts produced for / Creating bar charts to visualize cross-tabulation, How it works…, There's more…
• cross-validation / Cross-validation
• cross tab / Relationships between two categorical variables
• CSV (Comma-Separated Values) file
  • about / Importing and saving data from CSV files
• CSV files
  • data, importing from / Importing and saving data from CSV files
• Cubic Clustering Criterion
  • about / The k-means-based algorithm
• CUR decomposition
  • about / CUR decomposition
• curl utility
  • about / Downloading the complete text of web pages
• currency options
  • about / Currency options
• customer purchase data analysis
  • about / Customer purchase data analysis
• customized legends
  • adding, for multiple-line graphs / Adding customized legends for multiple-line graphs, How it works..., There's more..., See also
• cut points
  • about / Using numeric features with Naive Bayes
• cyl / Introduction

D

• 3D plot
  • about / Three-dimensional scatter plots
• DASL
  • about / Data source
  • URL / Data source
• data
  • loading, in R / Loading data into R, Working with packages
  • plotting, on Google maps / Plotting data on Google maps, How to do it..., How it works..., See also
  • about / The data
  • loading / Loading the data
  • obtaining, from open sources / Getting data from open sources
  • collecting / Collecting data
  • managing, with R / Managing data with R
  • importing, from CSV files / Importing and saving data from CSV files
• data.table package
  • using / Making data frames faster with data.table
  • URL / Making data frames faster with data.table
• data attributes
  • about / Data attributes and description
  • numeric attributes / Numeric attributes
  • categorical attributes / Categorical attributes
• data attributes, views
  • algebraic or geometric view / Data attributes and description
  • probability view / Data attributes and description
• databaseBasketball
  • URL / Getting ready
• Database Management Systems (DBMSs)
  • about / Querying data in SQL databases
• databases
  • about / Working with proprietary files and databases
  • data, querying in SQL databases / Querying data in SQL databases
• data classification
  • linearly separable / Document retrieval and Support Vector Machine
  • nonlinearly separable / Document retrieval and Support Vector Machine
• data cleaning
  • about / Data cleaning
  • missing values, avoiding / Missing values
  • junk / Junk, noisy data, or outlier
  • noisy data / Junk, noisy data, or outlier
  • outlier / Junk, noisy data, or outlier
• data description
  • about / Data attributes and description, Data description
  • measures of central tendency / Data description
  • measures of data dispersion / Data description
• data dictionary
  • about / Exploring the structure of data
• data dimension reduction
  • about / Data dimension reduction
  • eigenvalues / Eigenvalues and Eigenvectors
  • eigenvectors / Eigenvalues and Eigenvectors
  • PCA / Principal-Component Analysis
  • SVD / Singular-value decomposition
  • CUR decomposition / CUR decomposition
• data discretization
  • about / Data transformation and discretization, Data discretization
  • by binning / Data discretization
  • by histogram analysis / Data discretization
  • by cluster analysis / Data discretization
  • by decision tree analysis / Data discretization
  • by correlation analysis / Data discretization
• data exploration
  • about / Exploring and understanding data
• data formats
  • about / Other data formats
• data frame
  • about / Data frames
• data integration
  • about / Data integration
  • issues / Data integration
• data measuring
  • about / Data measuring
• data mining
  • about / Data mining, The origins of machine learning
  • feature extraction / Feature extraction
  • summarization / Summarization
  • process / The data mining process
  • statistics / Statistics and data mining
• data munging
  • about / Working with proprietary files and databases
• data points
  • grouping, within scatter plots / Grouping data points within a scatter plot, How it works..., There's more..., See also
  • labeling / Labeling data points, How it works..., There's more...
  • density, displaying on axes / Displaying the data density on axes, How it works..., There's more...
• data preparation
  • about / Data preparation
  • data source, calling / Data source at first glance
  • cash-flow generator functions / Cash-flow generator functions
  • cash-flow, preparing / Preparing the cash-flow
• data preparation, breast cancer example
  • training, creating / Data preparation – creating training and test datasets
  • test datasets, creating / Data preparation – creating training and test datasets
• Data Quality (DQ)
  • about / Data cleaning
• data selection / Data selection
• dataset
  • link-based features / Web spam detection
  • content-based features / Web spam detection
  • using / The dataset used in our examples
• data smoothing
  • binning / Junk, noisy data, or outlier
  • regression / Junk, noisy data, or outlier
  • classification / Junk, noisy data, or outlier
  • outlier / Junk, noisy data, or outlier
• data source
  • about / Data source
  • online resources / Data source
  • calling / Data source at first glance
• Data Source Name (DSN)
  • about / Querying data in SQL databases
• data storage / Data storage
• data structures, R
  • about / R data structures
  • vector / Vectors
  • factor / Factors
  • lists / Lists
  • data frame / Data frames
  • matrix / Matrixes and arrays
  • array / Matrixes and arrays
  • saving / Saving, loading, and removing R data structures
  • loading / Saving, loading, and removing R data structures
  • removing / Saving, loading, and removing R data structures
  • exploring / Exploring the structure of data
• data table
  • about / Making data frames faster with data.table
• data transformation
  • about / Data transformation and discretization, Data transformation
  • smoothing / Data transformation
  • attribute construction / Data transformation
  • aggregation / Data transformation
  • normalization / Data transformation
  • discretization / Data transformation
  • concept hierarchy generation, for nominal data / Data transformation
  • normalization methods / Normalization data transformation methods
• data visualization
  • lattice package used / Introduction
• data warehouse (DWH) / Data preparation
• data wrangling
  • about / Working with proprietary files and databases
• date variable
  • plotting, on X axis / Plotting the date or time variable on the x axis, How to do it..., How it works...
• day / Introduction
• DBSCAN algorithm
  • about / Customer categorization analysis of e-commerce and DBSCAN
  • characteristics / Customer categorization analysis of e-commerce and DBSCAN
  • pseudocode / The DBSCAN algorithm
  • customer categorization analysis, of e-commerce / Customer categorization analysis of e-commerce
• ddply()function / Getting ready
• decision nodes
  • about / Understanding decision trees
• decision tree
  • about / Generic decision tree induction, Understanding decision trees, Example – identifying risky bank loans using C5.0 decision trees
  • potential uses / Understanding decision trees
  • divide and conquer / Divide and conquer
  • pruning / Pruning the decision tree
  • used, for identifying risky bank loans / Example – identifying risky bank loans using C5.0 decision trees
  • accuracy, boosting / Boosting the accuracy of decision trees
• decision tree forests
  • about / Random forests
• decision tree induction
  • about / Generic decision tree induction
  • characteristics / Generic decision tree induction
  • attribute selection measures / Attribute selection measures
  • tree pruning / Tree pruning
  • algorithm, pseudocode / General algorithm for the decision tree generation
  • R implementation / The R implementation
• decision tree induction, attribute selection measures
  • Entropy / Attribute selection measures
  • Gain / Attribute selection measures
  • Gain Ratio / Attribute selection measures
  • Information Gain / Attribute selection measures
  • Gini Index / Attribute selection measures
  • Split Info / Attribute selection measures
• decision trees
  • about / Decision trees
  • classification rules, obtaining from / Rules from decision trees
• deep learning
  • about / The direction of information travel
• Deep Neural Network (DNN)
  • about / The direction of information travel
• default package
  • used, for base graphics / Base graphics using the default package
• degrees of freedom / Populations, samples, and estimation
• delimiter
  • about / Importing and saving data from CSV files
• delta hedge performance
  • comparing / Comparing the performance of delta hedging
• DENCLUE algorithm
  • about / Visitor analysis in the browser cache and DENCLUE
  • density attractor / Visitor analysis in the browser cache and DENCLUE
  • influence function / Visitor analysis in the browser cache and DENCLUE
  • density function / Visitor analysis in the browser cache and DENCLUE
  • gradient / Visitor analysis in the browser cache and DENCLUE
  • pseudocode / The DENCLUE algorithm
  • R implementation / The R implementation
  • visitor analysis, in browser cache / Visitor analysis in the browser cache
• dendrites
  • about / From biological to artificial neurons
• dendrograms
  • URL / How it works...
• density() function / There's more...
• density, data points
  • displaying, on axes / Displaying the data density on axes, How it works..., There's more...
• density-based cluster
  • about / Customer categorization analysis of e-commerce and DBSCAN
• density-based methods
  • about / Intrusion detection and density-based methods
  • OPTICS-OF algorithm / The OPTICS-OF algorithm
  • High Contrast Subspace (HiCS) algorithm / The High Contrast Subspace algorithm
  • R implementation / The R implementation
  • intrusion detection / Intrusion detection
• density line
  • overlaying, over histograms / Overlaying a density line over a histogram, How it works...
• density plot
  • creating, ggplot2 library used / Visualizing the density of a numeric variable, How it works…
• density plots
  • creating / Creating histograms and density plots, How to do it..., How it works..., There's more..., See also
• dependent events / Understanding joint probability
• dependent variable
  • about / Understanding regression
• derivatives
  • hedging / Hedging of derivatives
  • market risk / Market risk of derivatives
  • static delta hedge / Static delta hedge
  • dynamic delta hedge / Dynamic delta hedge
  • delta hedge performance, comparing / Comparing the performance of delta hedging
• descriptive model / Types of machine learning algorithms
• dev.off() command / How it works...
• diagonal decision boundary / The diagonal decision boundary
• dimensions
  • setting / Getting ready, See also
• directed graphs
  • about / Graph
• directional hypothesis / One and two-tailed tests
• discrete, numeric attributes
  • about / Numeric attributes
• discrete numeric variable / Univariate data
• discriminative frequent pattern-based classification
  • about / Discriminative frequent pattern-based classification
  • pseudocode / Discriminative frequent pattern-based classification
• disjunctive normal form (DNF)
  • about / Web sentiment analysis and CLIQUE
• disk-based data frames
  • creating, with ff package / Creating disk-based data frames with ff
• disp / Introduction
• distance-based outlier detection algorithm
  • about / The distance-based algorithm
• distributions
  • visualizing, kernel-density plot used / Visualizing distributions through a kernel-density plot, How to do it…, How it works…, There's more…
• divide and conquer
  • about / Divide and conquer
• divisive clustering
  • about / News categorization and hierarchical clustering
• document retrieval
  • with SVM algorithm / Document retrieval
• document text
  • automatic abstraction, k-medoids algorithm used / Automatic abstraction and summarization of document text
• Dolphin algorithm
  • about / The Dolphin algorithm
• domain-specific data
  • working with / Working with domain-specific data
  • bioinformatics data, analyzing / Analyzing bioinformatics data
  • network data, analyzing / Analyzing and visualizing network data
  • network data, visualizing / Analyzing and visualizing network data
• doParallel package
  • using / Taking advantage of parallel with foreach and doParallel
• dotchart() function / How to do it..., How it works...
• dotchart function / Base graphics using the default package
• dot charts
  • modifying, with grouped variables / Modifying dot charts by grouping variables, How to do it..., How it works...
• dots per inch (dpi) / There's more...
• double-knock-in (DKI)
  • about / A glance beyond vanillas
• double-knock-out (DKO)
  • about / A glance beyond vanillas
• Double-no-touch (DNT)
  • about / How R can help a lot
• Double-no-touch option
  • pricing / Pricing the Double-no-touch option
  • pricing, alternate way / Another way to price the Double-no-touch option
  • defining / The life of a Double-no-touch option – a simulation
• Double-one-touch (DOT) / The life of a Double-no-touch option – a simulation
• Dow Jones Industrial Average index (DIJA) / Neural networks
• dplyr package
  • used, for generalizing tabular data structures / Generalizing tabular data structures with dplyr
  • URL / Generalizing tabular data structures with dplyr
• drat / Introduction
• dummy coding / Preparing data for use with k-NN, Step 3 – training a model on the data
• dummy variable / Examining relationships – two-way cross-tabulations, Step 3 – training a model on the data
• dynamic delta hedge / Dynamic delta hedge
• dynamic hedging
  • about / The role of dynamic hedging

E

• e-commerce
  • customer categorization analysis / Customer categorization analysis of e-commerce
• early stopping
  • about / Pruning the decision tree
• Eclat algorithm
  • about / The Eclat algorithm
  • R implementation / The R implementation
• edgelist
  • about / Analyzing and visualizing network data
• EGARCH model / The Exponential GARCH model (EGARCH)
• eigenvalues
  • about / Eigenvalues and Eigenvectors
• eigenvectors
  • about / Eigenvalues and Eigenvectors
• elements
  • about / Vectors
• EMA / SMA and EMA
• Emacs Speaks Statistics (ESS) / R Scripting
• embarrassingly parallel problems
  • about / Learning faster with parallel computing
• EM methods
  • about / Ensemble (EM) methods
  • structure / Ensemble (EM) methods
  • bagging algorithm / The bagging algorithm
  • AdaBoost algorithm / The boosting and AdaBoost algorithms
  • boosting algorithm / The boosting and AdaBoost algorithms
  • Random forests algorithm / The Random forests algorithm
  • R implementation / The R implementation
  • parallel version, with MapReduce / Parallel version with MapReduce
• empirical Cumulative Distribution Function
  • visualizing / Visualizing an empirical Cumulative Distribution Function, How it works…, There's more…
• ensemble learning / Random forests
• ensemble methods
  • bagging / Bagging
  • boosting / Boosting
  • random forests / Random forests
• ensembles
  • about / Understanding ensembles
  • advantages / Understanding ensembles
• entropy
  • about / Choosing the best split
• epoch
  • about / Training neural networks with backpropagation
  • forward phase / Training neural networks with backpropagation
  • backward phase / Training neural networks with backpropagation
• erosion / Simple linear regression
• errbar function
  • about / There's more...
• error bars
  • adding / Adding error bars, How to do it..., How it works...
  • bar charts, creating with / Creating a bar chart with error bars, Getting ready, How it works…, There's more…
• escription length (DL)
  • about / The RIPPER algorithm
• ESRI shapefiles
  • working with / Working with ESRI shapefiles, How it works..., There's more
• estimation / Populations, samples, and estimation
• Euclidean norm / The case of linearly separable data
• evaluation / Evaluation
• exchange options
  • about / Exchange options
  • two-dimensional Wiener processes / Two-dimensional Wiener processes
  • Margrabe formula / The Margrabe formula
  • application, in R / Application in R
• exotic options
  • about / A glance beyond vanillas
  • embedded, in structured products / Exotic options embedded in structured products
• Expectation Maximization (EM) algorithm
  • about / User search intent and the EM algorithm
  • pseudocode / The EM algorithm
  • R implementation / The R implementation
  • user search intent, determining / The user search intent
• Expected Shortfall (ES) / Monte-Carlo simulation
• exponential moving average (EMA) / Built-in indicators
• exposure at default (EAD) / Minimum capital requirements
• expression() function / There's more...
• External Credit Assessment Institutions (ECAI) / Minimum capital requirements
• extract, transform, and load (ETL) / Data preparation

F

• 10-fold cross-validation (10-fold CV) / Cross-validation
• F-measure / The F-measure
• F-score / The F-measure
• F1 score / The F-measure
• factor
  • about / Factors
• Fama-French model
  • estimating / Estimation of the Fama-French model
• Fama-French three-factor model / Fama-French three-factor model
• FCA-based association rule mining algorithm
  • used, for web usage mining / The FCA-based association rule mining algorithm
  • R implementation / The R implementation
• feature extraction, examples
  • frequent itemsets / Feature extraction
  • similar items / Feature extraction
• Federal Reserve Economic Data (FRED) / Getting data from open sources
• feed-forward neural networks (FFNN) / Neural networks
• feedforward networks
  • about / The direction of information travel
• ffbase project
  • URL / Creating disk-based data frames with ff
• ff package
  • used, for creating disk-based data frames / Creating disk-based data frames with ff
  • URL / Creating disk-based data frames with ff
• filled.contour() function / How it works...
• filled contour plots
  • creating / Creating filled contour plots, How to do it..., How it works..., There's more, See also
• filtering
  • plot / Getting ready, There's more...
• FindAllOutsD algorithm
  • about / The FindAllOutsD algorithm
• FindAllOutsM algorithm
  • about / The FindAllOutsM algorithm
• findInterval() function / How it works...
• five-number summary / Measuring spread – quartiles and the five-number summary
• flow of control constructs / Flow of control
• font families
  • selecting, under Windows / Choosing font families and styles under Windows, Mac OS X, and Linux, How it works..., There's more
  • selecting, under Mac OS X / Choosing font families and styles under Windows, Mac OS X, and Linux, How it works..., There's more
  • selecting, under Linux / Choosing font families and styles under Windows, Mac OS X, and Linux, How it works..., There's more
• fonts
  • setting, for annotations / Setting fonts for annotations and titles, There's more..., See also
  • setting, for titles / Setting fonts for annotations and titles, There's more..., See also
  • selecting, for PostScripts / Choosing fonts for PostScripts and PDFs, How it works..., There's more
  • selecting, for PDFs / Choosing fonts for PostScripts and PDFs, How it works..., There's more
• foreach package
  • using / Taking advantage of parallel with foreach and doParallel
• FP-growth algorithm
  • about / The FP-growth algorithm
  • input data characteristics / Input data characteristics and data structure
  • data structure / Input data characteristics and data structure
  • pseudo code / The FP-growth algorithm
  • R implementation / The R implementation
• FRED (Federal Reserve Economic Data)
  • about / VAR implementation example
• frequency distributions
  • about / Frequency distributions
  • examples / Frequency distributions
• frequent itemset
  • about / The frequent itemset
• Frequent Itemset Mining Dataset Repository
  • URL / Data source
  • about / Data source
• frequently purchased groceries
  • identifying, with association rules / Example – identifying frequently purchased groceries with association rules
• frequent patterns
  • about / Patterns and pattern discovery
  • frequent itemset / Patterns and pattern discovery, The frequent itemset
  • frequent substructures / Patterns and pattern discovery, The frequent substructures
  • frequent subsequence / Patterns and pattern discovery, The frequent subsequence
• frequent subgraph patterns mining algorithm
  • about / Mining frequent subgraph patterns
  • gPLS algorithm / Mining frequent subgraph patterns
  • GraphSig algorithm / The gSpan algorithm
  • gSpan algorithm / The gSpan algorithm
  • R implementation / The R implementation
• frequent subsequence
  • about / The frequent subsequence
  • examples / The frequent subsequence
• frequent substructures
  • about / The frequent substructures
  • examples / The frequent substructures
• functions / Functions
  • plotting, for variable in dataset / Plotting functions of a variable in a dataset, How it works..., There's more...
• fundamental analysis
  • about / The basics of fundamental analysis
• fundamental equity strategy
  • building / The basics of fundamental analysis
• future performance
  • estimating / Estimating future performance
• future performance estimation
  • holdout method / The holdout method
  • cross-validation / Cross-validation
  • bootstrap sampling / Bootstrap sampling
• future prices
  • predicting / Predicting future prices
• FX
  • about / Markets
• FX rates
  • about / Terminology and notations

G

• GADM
  • URL / There's more...
  • about / There's more...
• GADM data
  • URL / There's more
• GARCH model
  • about / Further extensions
• GARCH modeling, with rugarch package
  • about / GARCH modeling with the rugarch package
  • Standard GARCH model / The standard GARCH model
  • EGARCH model / The Exponential GARCH model (EGARCH)
  • TGARCH model / The Threshold GARCH model (TGARCH)
• Gaussian distribution / Central tendency
• Gaussian RBF kernel / Using kernels for non-linear spaces
• gear / Introduction
• generalization / Generalization
• Generalized Linear Model (GLM) / Logistic regression
• Generalized Linear Models (GLM) / Understanding regression
• general pricing approach
  • about / A general pricing approach
• GenMax algorithm
  • about / The GenMax algorithm with maximal frequent itemsets
  • R implementation / The R implementation
• genre categorization
  • of web pages / Genre categorization of web pages
• Geographical Information Systems (GIS) data formats / Introduction
• geometric Brownian motion (GBM) / Dynamic delta hedge
• geom_bar() function / There's more…
• get.hist.quote() function
  • about / How it works...
• ggplot2
  • about / Visualization methods
  • using / Visualization methods
• ggplot2 library
  • about / Introduction
  • used, for creating bar charts / Creating bar charts, Getting ready, How it works…, See also
  • used, for creating density plot / Visualizing the density of a numeric variable, How it works…
  • used, for creating box plots / Creating a box plot, How it works…
  • line charts, creating with / Creating a line chart, How it works…, There's more...
  • graph, annotating with / Graph annotation with ggplot, How to do it..., How it works...
• ggplot2 package
  • using / Getting ready
• GLM (general linear model) / Estimation of the Fama-French model
• global data
  • plotting by countries, on world map / Plotting global data by countries on a world map, How to do it..., How it works..., See also
• glyph / Step 1 – collecting data
• Google maps
  • data, plotting / Plotting data on Google maps, How to do it..., How it works..., There's more
• GPU
  • about / GPU computing
  • computing / GPU computing
  • URL / GPU computing
• gradient descent / Training neural networks with backpropagation
• Grammar of Graphics
  • graphs, inspired by / Graphs inspired by Grammar of Graphics
• graph
  • annotating, with ggplot2 library / Graph annotation with ggplot, How to do it..., How it works...
  • about / Graph
  • directed graphs / Graph
  • undirected graphs / Graph
• Graph-Based Sub-topic Partition Algorithm (GSPSummary) algorithm
  • about / The multidocument summarization algorithm
• graph and network data
  • clustering / SNS and clustering graph and network data
• graph margins
  • setting / Setting graph margins and dimensions, See also
• graph mining
  • about / Graph mining
  • algorithms / Graph mining algorithms
• Graph Modeling Language (GML)
  • about / Analyzing and visualizing network data
• graphs
  • inspired, by Grammar of Graphics / Graphs inspired by Grammar of Graphics
  • creating, with maps / Getting ready, How it works..., There's more...
  • saving / Saving and exporting graphs, How it works..., See also
  • exporting / How to do it..., How it works..., See also
  • creating, with regional maps / Creating graphs with regional maps, How it works..., There's more
  • exporting, in high-resolution image formats / Exporting graphs in high-resolution image formats – PNG, JPEG, BMP, and TIFF, How to do it..., How it works..., There's more
  • exporting, in vector formats / Exporting graphs in vector formats – SVG, PDF, and PS, How it works..., There's more
  • text descriptions, adding / Adding text descriptions to graphs, How to do it..., How it works..., There's more
• graph templates
  • using / Using graph templates, How it works...
• greedy learners / What makes trees and rules greedy?
• Greeks
  • about / Greeks – the link back to the vanilla world
  • delta / Greeks – the link back to the vanilla world
  • gamma / Greeks – the link back to the vanilla world
  • vega / Greeks – the link back to the vanilla world
  • theta / Greeks – the link back to the vanilla world
  • rho / Greeks – the link back to the vanilla world
• grid
  • about / Learning faster with parallel computing
• grid() function / How it works...
• gross incomes (GI) / Minimum capital requirements
• group argument / How it works…
• grouped data points
  • highlighting, by size / Highlighting grouped data points by size and symbol type, How to do it..., How it works...
  • highlighting, by symbol type / Highlighting grouped data points by size and symbol type, How to do it..., How it works...
• GSP algorithm
  • sequence dataset, mining / The GSP algorithm
  • features / The GSP algorithm
  • R implementation / The R implementation

H

• Hadoop
  • using / Parallel cloud computing with MapReduce and Hadoop
  • URL / Parallel cloud computing with MapReduce and Hadoop
• harmonic mean / The F-measure
• hash-tag / Arithmetic and assignment
• header line
  • about / Importing and saving data from CSV files
• heatmap() function / How to do it..., How it works...
• heatmap function / Base graphics using the default package
• heat maps
  • creating / Creating heat maps
  • working / How it works..., There's more...
  • creating, of single Z variable with scale / Getting ready, How it works..., See also
  • multivariate data, summarizing / Summarizing multivariate data in a single heat map, How to do it..., How it works...
• hedge optimization / Optimization of the hedge
• help.start() function / Getting help in R
• hError algorithm
  • about / The hError algorithm
  • R implementation / The R implementation
• hierarchical clustering
  • about / News categorization and hierarchical clustering
  • agglomerative clustering / News categorization and hierarchical clustering
  • divisive clustering / News categorization and hierarchical clustering
  • characteristics / News categorization and hierarchical clustering
  • BIRCH algorithm / The BIRCH algorithm
  • chameleon algorithm / The chameleon algorithm
  • Bayesian hierarchical clustering algorithm / The Bayesian hierarchical clustering algorithm
  • probabilistic hierarchical clustering algorithm / The probabilistic hierarchical clustering algorithm
  • R implementation / The R implementation
  • used, for news categorization / News categorization
• hierarchical clustering algorithm
  • about / Hierarchical clustering to detect outliers
• high-dimensional data
  • clustering / Customer purchase data analysis and clustering high-dimensional data
• high-level data visualization
  • functions / Base graphics using the default package
• high-performance algorithms
  • about / High-performance algorithms
• high-resolution image formats
  • graphs, exporting in / Exporting graphs in high-resolution image formats – PNG, JPEG, BMP, and TIFF, How to do it..., How it works...
• high-value credit card customers
  • classifying, ID3 algorithm used / High-value credit card customers classification using ID3, High-value credit card customers classification
• High Contrast Subspace (HiCS) algorithm
  • about / The High Contrast Subspace algorithm
• high frequency trading (HFT) / The TA toolkit
• HilOut algorithm
  • about / The HilOut algorithm
  • R implementation / The R implementation
• hist() function / How it works...
  • about / How it works...
• hist function / Base graphics using the default package
• histogram
  • about / Creating a conditional histogram
• histograms
  • creating / Creating histograms and density plots, How to do it...
  • working / How it works..., There's more..., See also
  • distributions, visualizing as count frequencies / Visualizing distributions as count frequencies or probability densities, How to do it..., How it works..., There's more
  • distributions, visualizing as probability densities / Visualizing distributions as count frequencies or probability densities, How to do it..., How it works..., There's more
  • bin size, setting / Setting the bin size and the number of breaks, How it works..., There's more
  • breaks, setting / Setting the bin size and the number of breaks, How it works..., There's more
  • bar colors, adjusting / Adjusting histogram styles – bar colors, borders, and axes, How it works..., There's more
  • borders, adjusting / Adjusting histogram styles – bar colors, borders, and axes, How it works..., There's more
  • axes, adjusting / Adjusting histogram styles – bar colors, borders, and axes, How it works..., There's more
  • density line, overlaying / Overlaying a density line over a histogram, How it works...
  • drawing, in margins of line plots / Histograms in the margins of line and scatter plots, How it works...
  • drawing, in margins of scatter plots / Histograms in the margins of line and scatter plots, How it works...
  / Visualizing numeric variables – histograms
• historical VaR / Historical VaR
• Hmisc package
  • about / There's more...
• holdout method / The holdout method, Cross-validation
• Holm-Bonferroni correction / Testing more than two means
• horizontal box plots
  • creating / Creating horizontal box plots, How it works...
• horizontal format
  • about / Input data characteristics and data structure
• horizontal grid lines
  • adding / Adding horizontal and vertical grid lines, How it works..., There's more..., See also
• hp / Introduction
• httr package
  • URL / Downloading the complete text of web pages
• hybrid association rules mining
  • about / Hybrid association rules mining
  • multilevel and multidimensional association rules mining / Hybrid association rules mining, Mining multilevel and multidimensional association rules
  • constraint-based frequent pattern mining / Hybrid association rules mining, Constraint-based frequent pattern mining
• hyperplane / Understanding Support Vector Machines
• Hypertext Markup Language (HTML)
  • about / Downloading the complete text of web pages

I

• ID3 algorithm
  • about / High-value credit card customers classification using ID3
  • used, for classifying high-value credit card customers / High-value credit card customers classification using ID3, High-value credit card customers classification
  • input parameters / The ID3 algorithm
  • output parameter / The ID3 algorithm
  • pseudocode / The ID3 algorithm
  • R implementation / The R implementation
  • used, for web attack detection / Web attack detection
• identification problem
  • about / Vector autoregressive models
• ifelse() function / Advanced subsetting
• igraph package
  • about / Analyzing and visualizing network data
  • URL / Analyzing and visualizing network data
• image() function / There's more..., How it works...
• image function / Base graphics using the default package
• implementation, in R
  • about / Implementation in R
  • data / The data
  • data, loading / Loading the data
  • seasonal component / The seasonal component
  • AR(1) model, estimating / AR(1) estimation and forecasting
  • AR(1) model, forecasting / AR(1) estimation and forecasting
  • SETAR model, estimating / SETAR estimation and forecasting
  • SETAR model, forecasting / SETAR estimation and forecasting
  • results, interpreting / Interpreting the results
• imputation
  • methods / Methods of imputation
  • multiple imputation / Multiple imputation in practice
  / Data preparation – imputing the missing values
• Incremental Reduced Error Pruning (IREP) algorithm / The RIPPER algorithm
• independence of proportions
  • statistical significance / Don't be fooled!
  • testing / Testing independence of proportions
• independent / Testing two means
• independent events / Understanding joint probability
• independent samples t-test
  • using / Testing two means
  • assumptions / Assumptions of the independent samples t-test
• independent variables
  • about / Understanding regression
• indexing / Subsetting
• industry specific investment
  • about / Industry-specific investment
• information gain / Choosing the best split
• input data
  • types / Types of input data
  • matching, to algorithms / Matching input data to algorithms
• input nodes / The number of layers
• instance-based learning
  • about / Why is the k-NN algorithm lazy?
• Integrated Development Environment (IDE) / R Scripting
• intensity of trading
  • about / The intensity of trading
• interaction terms / Advanced topics
• intercept
  • about / Understanding regression
• interest rate models
  • parameter, estimating of / Parameter estimation of interest rate models
• interest rate risk measurement
  • managing / Interest rate risk measurement
• Internal Ratings-Based (IRB) / Minimum capital requirements
• Internal Revenue Service (IRS) / Loading big data
• International Capital Adequacy Assessment Process (ICAAP) / Supervisory review
• interquartile range
  • using / Spread
• Interquartile Range (IQR) / Measuring spread – quartiles and the five-number summary
• interval-scaled
  • dissimilarity / Data measuring
• interval estimation
  • about / Interval estimation
  • qnorm function, using / How did we get 1.96?
• intrusion detection
  • about / Intrusion detection
• Intrusion Detection System (IDS)
  • about / Web attack detection
• inverse link function / Logistic regression
• investment strategy
  • about / A universally consistent, non-parametric investment strategy
  • evaluating / Evaluation of the strategy
• investment targets
  • separating / Separating investment targets
• IR
  • about / Information retrieval and text mining
• itemset
  • about / Understanding association rules
• iterative classification algorithms
  • about / The node classification and iterative classification algorithms
• Iterative Dichotomiser 3 (ID3) / The C5.0 decision tree algorithm
• Iteratively Re-Weighted Least Squares (IWLS) / A word of warning

J

• Javascript Object Notation (JSON)
  • about / Using JSON
• jitter() function
  • used, for distinguishing closely packed data points / Using jitter to distinguish closely packed data points, How to do it..., How it works...
• joint probability / Understanding joint probability
• JSON
  • parsing, from web APIs / Parsing JSON from web APIs
  • about / Parsing JSON from web APIs
  • URL / Parsing JSON from web APIs
• jsonlite package
  • URL / Parsing JSON from web APIs

K

• k-fold cross-validation (or k-fold CV) / Cross-validation
• k-itemset
  • about / The frequent itemset
• k-means++ / Using distance to assign and update clusters
• k-means algorithm
  • about / Search engines and the k-means algorithm, The k-means-based algorithm
  • search engine / Search engines and the k-means algorithm, Search engine and web page clustering
  • shortages / Search engines and the k-means algorithm
  • guidelines / Search engines and the k-means algorithm
  • pseudocode / The k-means clustering algorithm
  • kernel k-means algorithm, pseudocode / The kernel k-means algorithm
  • k-modes algorithm / The k-modes algorithm
  • R implementation / The R implementation
  • parallel version, with MapReduce / Parallel version with MapReduce
• k-means clustering, on big data
  • about / K-means clustering on big data
  • big matrices, loading / Loading big matrices
  • analysis / Big data K-means clustering analysis
• k-means clustering algorithm
  • about / The k-means clustering algorithm
• k-medoids algorithm
  • about / Automatic abstraction of document texts and the k-medoids algorithm
  • case considerations / Automatic abstraction of document texts and the k-medoids algorithm
  • PAM algorithm / The PAM algorithm
  • R implementation / The R implementation
  • used, for automatic abstraction of document text / Automatic abstraction and summarization of document text
• k-NN
  • using, in R / Using k-NN in R
  • limitations / Limitations of k-NN
• k-NN, using in R
  • about / Using k-NN in R
  • confusion matrices / Confusion matrices
• k-NN algorithm
  • about / The k-NN algorithm
  • weaknesses / The k-NN algorithm
  • similarity, measuring with distance / Measuring similarity with distance
  • appropriate k, selecting / Choosing an appropriate k
  • data, preparing / Preparing data for use with k-NN
  • lazy learning algorithm / Why is the k-NN algorithm lazy?
  • used, for diagnosing breast cancer / Example – diagnosing breast cancer with the k-NN algorithm
• kernel-density plot
  • distributions, visualizing through / Visualizing distributions through a kernel-density plot, How to do it…, There's more…
• kernel density estimation / Probability distributions
• kernels
  • using, for non-linear spaces / Using kernels for non-linear spaces
• kernel trick / Using kernels for non-linear spaces
• kernlab
  • reference / Step 3 – training a model on the data
• Keyhole Markup Language (KML) / Creating and reading KML data
• kitchen sink regression
  • about / Kitchen sink regression
• KML data
  • creating / Creating and reading KML data, How it works...
  • reading / Creating and reading KML data, How it works...
• KMV model
  • about / Credit risk
• kNN algorithm
  • about / Protein classification and the k-Nearest Neighbors algorithm, The kNN algorithm
  • used, for protein classification / Protein classification and the k-Nearest Neighbors algorithm
  • pseudocode / The kNN algorithm
  • R implementation / The R implementation
• knock-in (KI)
  • about / A glance beyond vanillas
• knock-in-knock-out (KIKO)
  • about / A glance beyond vanillas
• knock-out (KO)
  • about / A glance beyond vanillas
• Kruskal-Wallis test / What if my assumptions are unfounded?

L

• lambda functions / Functions
• Laplace estimator
  • about / The Laplace estimator
• large datasets
  • linear regression model, fitting on / Fitting a linear regression model on large datasets
  • managing / Managing very large datasets
  • tabular data structures, generalizing with dplyr / Generalizing tabular data structures with dplyr
  • data.table package, using / Making data frames faster with data.table
  • disk-based data frames, creating with ff package / Creating disk-based data frames with ff
  • massive matrices, using with bigmemory package / Using massive matrices with bigmemory
• Last.fm developer
  • URL / Using JSON
• latitude / Using kernels for non-linear spaces
• lattice
  • used, for Trellis graphs / Trellis graphs using lattice
• lattice package
  • using / Getting ready
  • used, for data visualization / Introduction
• layered plot
  • creating, with scatter plot / Creating a layered plot with a scatter plot and fitted line, How it works…
  • creating, with fitted line / Creating a layered plot with a scatter plot and fitted line, How it works…
• layers
  • about / The number of layers
• layout() function / How it works..., How it works...
• lazy learning algorithms / Why is the k-NN algorithm lazy?
• leaf nodes
  • about / Understanding decision trees
• learning rate / Training neural networks with backpropagation
• leave-one-out method / Cross-validation
• left-hand side (LHS) / Understanding association rules
• left-tailed / Central tendency
• legend() function / How to do it..., How it works..., Adding a legend to a pie chart, How it works...
• legends
  • adding / Adding and formatting legends, How it works..., There's more...
  • formatting / Adding and formatting legends, How it works..., There's more...
  • colors, setting / How it works..., There's more...
• levels / Types of machine learning algorithms
• library() command / Getting ready
• LIBSVM
  • URL / Step 3 – training a model on the data
• likelihood
  • about / Computing conditional probability with Bayes' theorem
• likelihood-based outlier detection algorithm
  • about / The likelihood-based outlier detection algorithm
  • R implementation / The R implementation
• linear kernel / Using kernels for non-linear spaces
• linear model lines
  • adding / Adding linear model lines, How it works...
• linear models
  • about / Linear models
• linear regression, diagnostics
  • about / Linear regression diagnostics
  • Anscombe relationship, second / Second Anscombe relationship
  • Anscombe relationship, third / Third Anscombe relationship
  • Anscombe relationship, fourth / Fourth Anscombe relationship
• linear regression model
  • fitting, on large datasets / Fitting a linear regression model on large datasets
• line charts
  • creating, with ggplot2 library / Creating a line chart, How it works…, There's more...
• line graphs
  • creating / Creating line graphs, How to do it...
  • working / How it works...
• lines
  • colors, setting / Getting ready, How to do it..., How it works..., There's more...
• lines() function / There's more..., How it works..., How it works...
• lines command / How it works...
• lines of business (LoB) / Minimum capital requirements
• line styles
  • selecting / Choosing line styles and width, How to do it..., How it works...
• link function / Logistic regression, Understanding regression
• Linux
  • URL / Introduction
  • font families, selecting under / Getting ready, How it works..., There's more
• liquidity coverage ratio (LCR) / Basel III
• liquidity risk measurement
  • managing / Liquidity risk measurement
• lists / Lists
• Local Outlier Factor (LOF)
  • about / Intrusion detection and density-based methods
• Local Reachability Density (LRD)
  • about / Intrusion detection and density-based methods
• loess curve / Visualizing relationships among features – the scatterplot matrix
• log axes
  • formatting / Formatting log axes, How it works..., There's more...
• logical data type / Logicals and characters
• logistic function / Logistic regression
• logistic regression
  • about / Logistic regression, Understanding regression
  • using / Logistic regression
  • using, in R / Using logistic regression in R
  • limitations / Using logistic regression in R
• logit function / Logistic regression
• logoptimal portfolios
  • about / Logoptimal portfolios
• longitude / Using kernels for non-linear spaces
• lookback options
  • about / A glance beyond vanillas
• loss given default (LGD) / Minimum capital requirements
• lowess() function
  • used, for adding nonparametric model curves / Adding nonparametric model curves with lowess, How it works...

M

• MACD / MACD
• machine / Using a bigger and faster machine
• machine learning
  • statistics / Statistics and machine learning
  • origins / The origins of machine learning
  • about / The origins of machine learning
  • abuses / Uses and abuses of machine learning
  • uses / Uses and abuses of machine learning
  • successes / Machine learning successes
  • limitations / The limits of machine learning
  • ethics / Machine learning ethics
  • process / How machines learn
  • with R / Machine learning with R
  • R packages, installing / Installing R packages
  • R packages, loading / Loading and unloading R packages
  • R packages, unloading / Loading and unloading R packages
• machine learning (ML)
  • about / Web data mining, Machine learning
  • architecture / Machine learning architecture
  • training and testing / Machine learning architecture
  • batch versus online learning / Machine learning architecture
  • feature selection / Machine learning architecture
  • training set, creating / Machine learning architecture
• machine learning (ML), classes
  • decision tree / Approaches to machine learning
  • perceptron / Approaches to machine learning
  • neural nets / Approaches to machine learning
  • instance-based learning / Approaches to machine learning
  • support-vector machines / Approaches to machine learning
• machine learning, in practice
  • about / Machine learning in practice
  • data collection / Machine learning in practice
  • data exploration and preparation / Machine learning in practice
  • model training / Machine learning in practice
  • model evaluation / Machine learning in practice
  • model improvement / Machine learning in practice
  • input data, types / Types of input data
  • algorithms, types / Types of machine learning algorithms
  • input data, matching to algorithms / Matching input data to algorithms
• machine learning, process
  • about / How machines learn
  • data storage / How machines learn, Data storage
  • abstraction / How machines learn, Abstraction
  • generalization / How machines learn, Generalization
  • evaluation / How machines learn, Evaluation
• machine learning algorithms
  • types / Types of machine learning algorithms
• Mac OS X
  • URL / Introduction
  • font families, selecting under / Choosing font families and styles under Windows, Mac OS X, and Linux, Getting ready, How it works..., There's more
• MAFIA algorithm
  • about / Customer purchase data analysis and clustering high-dimensional data
  • pseudocode / The MAFIA algorithm
  • customer purchase data analysis / Customer purchase data analysis
• magrittr package
  • about / Scraping data from web pages
  • URL / Scraping data from web pages
• Mann-Whitney U test / What if my assumptions are unfounded?
• MapReduce
  • C4.5 algorithm, parallel version / A parallel version with MapReduce
  • EM methods, parallel version / Parallel version with MapReduce
  • SVM algorithm, parallel version / Parallel version with MapReduce
  • BP algorithm, parallel version / Parallel version with MapReduce
  • k-means algorithm, parallel version / Parallel version with MapReduce
  • about / Parallel cloud computing with MapReduce and Hadoop
  • map step / Parallel cloud computing with MapReduce and Hadoop
  • reduce step / Parallel cloud computing with MapReduce and Hadoop
• maps
  • used, for creating graphs / Creating graphs with maps, How to do it..., How it works..., There's more...
• marginal likelihood
  • about / Computing conditional probability with Bayes' theorem
• margin labels
  • using, for multiple-line graphs / Using margin labels instead of legends for multiple-line graphs, How it works...
• Margin of static replication
  • about / Static replication of non-maturity deposits
• Margrabe formula / The Margrabe formula
• marker lines
  • adding, abline() function used / Adding marker lines at specific x and y values using abline, How it works..., There's more...
• market basket analysis
  • about / Market basket analysis
  • market basket model / The market basket model
  • A-Priori algorithm / A-Priori algorithms
  • Eclat algorithm / The Eclat algorithm
  • FP-growth algorithm / The FP-growth algorithm
  • GenMax algorithm / The GenMax algorithm with maximal frequent itemsets
  • Charm algorithm / The Charm algorithm with closed frequent itemsets
  • association rules, generating / The algorithm to generate association rules
• market basket analysis example
  • data, collecting / Step 1 – collecting data
  • data, preparing / Step 2 – exploring and preparing the data
  • data, exploring / Step 2 – exploring and preparing the data
  • sparse matrix, creating for transaction data / Data preparation – creating a sparse matrix for transaction data
  • item support, visualizing / Visualizing item support – item frequency plots
  • transaction data, visualizing / Visualizing the transaction data – plotting the sparse matrix
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
  • set of association rules, sorting / Sorting the set of association rules
  • subset of association rules, sorting / Taking subsets of association rules
  • association rules, saving to file / Saving association rules to a file or data frame
  • association rules, saving to data frame / Saving association rules to a file or data frame
• market basket model
  • about / The market basket model
• market efficiency / Market efficiency
• market risk / Market risk
• market risk, of derivatives
  • about / Market risk of derivatives
• mathematical notations
  • adding / Adding mathematical and scientific notations (typesetting), How to do it..., How it works...
• matrix
  • creating / Matrices
  • about / Matrices, Matrixes and arrays
  / Matrixes and arrays
• matrix notation / Multiple linear regression
• maturity (M) / Minimum capital requirements
• maximal frequent itemset (MFI)
  • about / The GenMax algorithm with maximal frequent itemsets
  • mining, with GenMax algorithm / The GenMax algorithm with maximal frequent itemsets
• Maximal Marginal Relevance (MMR) algorithm
  • about / The Maximal Marginal Relevance algorithm
  • R implementation / The R implementation
• Maximum Likelihood Estimation (MLE) / Logistic regression
  • about / User search intent and the EM algorithm
• maximum margin hyperplane (MMH) / Classification with hyperplanes
• mean / Measuring the central tendency – mean and median
• mean absolute error (MAE) / Measuring performance with the mean absolute error
• mean height
  • estimating / Estimating means
• Mean Squared Error (MSE) / Simple linear regression
• measures of spread
  • for categorical data / Spread
• medical expenses, predicting with linear regression
  • about / Example – predicting medical expenses using linear regression
  • data, collecting / Step 1 – collecting data
  • data, preparing / Step 2 – exploring and preparing the data
  • data, exploring / Step 2 – exploring and preparing the data
  • correlation matrix / Exploring relationships among features – the correlation matrix
  • relationships, visualizing among features / Visualizing relationships among features – the scatterplot matrix
  • scatterplot matrix / Visualizing relationships among features – the scatterplot matrix
  • model, training on data / Step 3 – training a model on the data
  • model performance, training / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance, Model specification – adding non-linear relationships, Transformation – converting a numeric variable to a binary indicator, Model specification – adding interaction effects, Putting it all together – an improved regression model
• message-passing interface (MPI)
  • about / Working in parallel with multicore and snow
• meta-learners / Types of machine learning algorithms
• meta-learning methods
  • used, for improving model performance / Improving model performance with meta-learning
  • about / Improving model performance with meta-learning
• mfcol argument / How it works...
• mfrow argument / How it works...
• min-max normalization / Preparing data for use with k-NN
• minimum capital requirements / Minimum capital requirements
• missing data
  • analysis / Analysis with missing data
  • visualizing / Visualizing missing data
  • types / Types of missing data, So which one is it?
  • methods, for dealing / Unsophisticated methods for dealing with missing data
  • complete case analysis / Complete case analysis
  • pairwise distribution / Pairwise deletion
  • mean substitution / Mean substitution
  • hot deck imputation / Hot deck imputation
  • regression imputation / Regression imputation
  • stochastic regression imputation / Stochastic regression imputation
  • multiple imputation / Multiple imputation, So how does mice come up with the imputed values?
  • out-of-bounds data, checking for / Checking for out-of-bounds data
  • column data type, checking / Checking the data type of a column
  • unexpected categories, checking / Checking for unexpected categories
  • outliers, checking for / Checking for outliers, entry errors, or unlikely data points
  • entry errors, checking / Checking for outliers, entry errors, or unlikely data points
  • unlikely data points, checking / Checking for outliers, entry errors, or unlikely data points
  • outliers, checking / Checking for outliers, entry errors, or unlikely data points
  • assertions, chaining / Chaining assertions
• missing values
  • avoiding / Missing values
  • considerations / Missing values
• mixed data
  • visualizing / Visualizing mixed data
• mobile fraud detection
  • about / Activity monitoring and the detection of mobile fraud
• mobile phone spam
  • filtering, with Naive Bayes algorithm / Example – filtering mobile phone spam with the Naive Bayes algorithm
• mobile phone spam example
  • data, collecting / Step 1 – collecting data
  • dat a collecting, URL / Step 1 – collecting data
  • data, preparing / Step 2 – exploring and preparing the data
  • data, exploring / Step 2 – exploring and preparing the data
  • text data, cleaning / Data preparation – cleaning and standardizing text data
  • text data, standardizing / Data preparation – cleaning and standardizing text data
  • text documents, splitting into words / Data preparation – splitting text documents into words
  • training, creating / Data preparation – creating training and test datasets
  • test datasets, creating / Data preparation – creating training and test datasets
  • text data, visualizing / Visualizing text data – word clouds
  • indicator features, creating for frequent words / Data preparation – creating indicator features for frequent words
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
• model, of deposit interest rate development / A Model of deposit interest rate development
• modeling, in R
  • about / Modeling in R
  • data selection / Data selection
  • APT, estimating with principal component analysis / Estimation of APT with principal component analysis
  • Fama-French model, estimating / Estimation of the Fama-French model
• model performance
  • improving, with meta-learning / Improving model performance with meta-learning
• model performance, breast cancer example
  • z-score standardization / Transformation – z-score standardization
  • alternatives values, testing of k / Testing alternative values of k
• model trees / Understanding regression trees and model trees
• money management
  • about / A word on money management
• Monte-Carlo simulation / Monte-Carlo simulation
• month / Introduction
• mpg / Introduction
• multi-layer precepton (MLP) / Neural networks
• multicore package
  • using / Working in parallel with multicore and snow
• multidocument summarization algorithm
  • about / The multidocument summarization algorithm
• multilayer network
  • about / The number of layers
• Multilayer Perceptron (MLP)
  • about / The direction of information travel
• multilevel and multidimensional association rules mining
  • about / Mining multilevel and multidimensional association rules
• multimodal / Measuring the central tendency – the mode
• multinomial logistic regression / Understanding regression
• multiple-line graphs
  • customized legends, adding for / Adding customized legends for multiple-line graphs, How it works..., There's more..., See also
  • margin labels, using / Using margin labels instead of legends for multiple-line graphs, How it works...
• multiple bar charts
  • creating / Creating multiple bar charts, Getting ready, How it works…, There's more…, See also
• multiple correlations
  • comparing / Comparing multiple correlations
• multiple histograms
  • creating, along diagonal of pairs plot / Multiple histograms along the diagonal of a pairs plot, How it works...
• multiple linear regression / Understanding regression
  • about / Multiple linear regression
  • weaknesses / Multiple linear regression
• multiple means
  • testing / Testing more than two means
• multiple plot matrix layouts
  • creating / Creating multiple plot matrix layouts
  • working / How it works..., There's more...
• multiple R-squared value (coefficient of determination) / Step 4 – evaluating model performance
• multiple regression
  • about / Multiple regression
• multiple variables
  • including / Including multiple variables
• multivariate continuous data visualization
  • about / Multivariate continuous data visualization, How it works…, There's more…
• multivariate data
  • about / Multivariate data
  • summarizing, in heat maps / Summarizing multivariate data in a single heat map, How to do it..., How it works...
• multivariate relationships
  • about / Exploring relationships between variables
• multivariate time series analysis
  • about / Multivariate time series analysis
  • cointegration / Cointegration
  • VAR / Vector autoregressive models
  • VAR and VECM, cointegrated / Cointegrated VAR and VECM
• multivariate visualization
  • of categorical data / Multivariate categorical data visualization, How it works…, There's more…
• MusicBrainz
  • URL / XML

N

• 1NN classifier algorithm
  • about / Time-series classification with the 1NN classifier
• N-gram-based text-categorization algorithm
  • used, for categorizing newspaper articles / Categorizing newspaper articles and newswires into topics
  • used, for categorizing newswires / Categorizing newspaper articles and newswires into topics
  • about / The N-gram-based text categorization
  • pseudocode / The N-gram-based text categorization
  • R implementation / The R implementation
• Naive Bayes algorithm
  • about / Understanding Naive Bayes, The Naive Bayes algorithm
  • classification / Classification with Naive Bayes
  • Laplace estimator / The Laplace estimator
  • numeric features, using with / Using numeric features with Naive Bayes
  • used, for filtering mobile phone spam / Example – filtering mobile phone spam with the Naive Bayes algorithm
• Naïve Bayes classification
  • used, for identifying spam e-mail / Identify spam e-mail and Naïve Bayes classification, Identify spam e-mail
  • characteristics / Identify spam e-mail and Naïve Bayes classification
  • pseudocode / The Naïve Bayes classification
  • R implementation / The R implementation
• nBin argument / There's more…
• nearest neighbor classification
  • about / Understanding nearest neighbor classification
• negatively skewed / Central tendency
• Net stable funding ratio (NSFR) / Basel III
• network analysis
  • about / Analyzing and visualizing network data
• network data
  • analyzing / Analyzing and visualizing network data
  • visualizing / Analyzing and visualizing network data
• network topology
  • about / Network topology
  • layers / The number of layers
  • direction of information travel / The direction of information travel
  • number of nodes in each layer / The number of nodes in each layer
• neural networks
  • about / Understanding neural networks
  • biological, to artificial neurons / From biological to artificial neurons
  • characteristics / From biological to artificial neurons
  • training, with backpropagation / Training neural networks with backpropagation
• neural networks (NN)
  • about / Neural networks
• neurons
  • about / Understanding neural networks
• news categorization
  • with hierarchical clustering / News categorization
• NHST
  • about / Null Hypothesis Significance Testing
  • default hypothesis / Null Hypothesis Significance Testing
  • one-tailed test / One and two-tailed tests
  • two-tailed tests / One and two-tailed tests
  • Type I error / When things go wrong
  • Type II error / When things go wrong
  • significance, warning / A warning about significance
  • p-values, warning / A warning about p-values
• NL algorithm
  • about / The NL algorithm
• nls() function / How it works...
• nodes / Understanding neural networks
• nominal / Types of input data
• nominal attributes
  • dissimilarity / Data measuring
• nominal variables
  • about / Factors
• non-binary predictor
  • regression with / Regression with a non-binary predictor
• non-linear modeling / Advanced topics
• non-linear spaces
  • kernels, using for / Using kernels for non-linear spaces
• non-maturity deposits (NMD)
  • modeling / Modeling non-maturity deposits
  • model, of deposit interest rate development / A Model of deposit interest rate development
  • static replication, of non-maturity deposits / Static replication of non-maturity deposits
• nonlinear model curves
  • adding / Adding nonlinear model curves, How it works...
• nonparametric model curves
  • adding, with lowess() function / Adding nonparametric model curves with lowess, How it works...
• normal distribution
  • about / The normal distribution
  • three-sigma rule / The three-sigma rule and using z-tables
  • z-tables, using / The three-sigma rule and using z-tables
  • fitting, to precipitation dataset / Fitting distributions the Bayesian way
  / Understanding numeric data – uniform and normal distributions
• normalization methods, data transformation
  • min-max normalization / Normalization data transformation methods
  • z-score normalization / Normalization data transformation methods
  • normalization by decimal scaling / Normalization data transformation methods
• normal Q-Q plot
  • creating / Creating a normal Q-Q plot, How it works…
• Not a Number (NaN) / Arithmetic and assignment
• not available (NA) / Subsetting
• null hypothesis terminology / Null Hypothesis Significance Testing
• numeric / Types of input data
• numeric attributes
  • about / Numeric attributes
• numeric attributes, types
  • interval-scaled / Numeric attributes
  • ratio-scaled / Numeric attributes
• numeric data
  • about / Understanding numeric data – uniform and normal distributions
  • normalizing / Transformation – normalizing numeric data
• numeric features
  • using, with Naive Bayes / Using numeric features with Naive Bayes
• numeric prediction / Types of machine learning algorithms
• numeric variables
  • about / Exploring numeric variables
  • central tendency, measuring / Measuring the central tendency – mean and median
  • spread, measuring / Measuring spread – quartiles and the five-number summary, Measuring spread – variance and standard deviation
  • visualizing / Visualizing numeric variables – boxplots, Visualizing numeric variables – histograms

O

• OCR, performing with SVMs
  • about / Example – performing OCR with SVMs
  • data, collecting / Step 1 – collecting data
  • data, exploring / Step 2 – exploring and preparing the data
  • data, preparing / Step 2 – exploring and preparing the data
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
• OCSVM (One Class SVM) algorithm
  • about / The OCSVM algorithm
• ODIN algorithm
  • about / The ODIN algorithm
• one-class nearest neighbor algorithm
  • about / The one-class nearest neighbor algorithm
• one-tailed hypothesis test
  • running / Testing the mean of one sample
• one-tailed test / One and two-tailed tests
• one-way table / Exploring categorical variables
• one sample t-test
  • about / Testing the mean of one sample
  • assumptions / Assumptions of the one sample t-test
• online data
  • working with / Working with online data and services
  • parsing / Working with online data and services
  • complete text of web pages, downloading / Downloading the complete text of web pages
  • parsing, within web pages / Scraping data from web pages
• online repositories
  • about / Online repositories
• online services
  • working with / Working with online data and services
• Open Database Connectivity (ODBC)
  • about / Querying data in SQL databases
• OpenRefine / OpenRefine
• open sources
  • data, obtaining from / Getting data from open sources
• operational risk
  • about / Operational risk
  • low impact with low probability / Operational risk
  • low impact with high probability / Operational risk
  • high impact with low probability / Operational risk
  • high impact with high probability / Operational risk
• opinion-orientation algorithm
  • about / Opinion mining
• opinion mining
  • about / Opinion mining
• OPTICS-OF algorithm
  • about / The OPTICS-OF algorithm
• OPTICS algorithm
  • about / Clustering web pages and OPTICS, The OPTICS algorithm
  • core-distance of object / Clustering web pages and OPTICS
  • reachability-distance of object / Clustering web pages and OPTICS
  • pseudocode / The OPTICS algorithm
  • R implementation / The R implementation
  • web pages, clustering / Clustering web pages
• optimized learning algorithms
  • deploying / Deploying optimized learning algorithms
  • regression models, building with biglm package / Building bigger regression models with biglm
  • random forests, building with bigrf package / Growing bigger and faster random forests with bigrf
  • models in parallel, evaluating with caret package / Training and evaluating models in parallel with caret
• optimized packages
  • using / Using optimized packages
• optimizing
  • ways / Wait to optimize
• order() function / How it works...
• ordinal / Types of input data
• ordinal attributes
  • dissimilarity / Data measuring
• ordinary least squares estimation
  • about / Ordinary least squares estimation
• Out-Of-Bag (OOB) / Random forests
• out-of-bag error rate / Training random forests
• outlier detection
  • with statistical method / Credit card fraud detection and statistical methods
  • proximity-based methods / Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods
  • density-based methods / Intrusion detection and density-based methods
  • clustering-based methods / Intrusion detection and clustering-based methods
  • classification-based methods / Monitoring the performance of the web server and classification-based methods
  • topic detection / Detecting novelty in text and topic detection
  • novelty, detecting in text / Detecting novelty in text and topic detection
  • in high-dimensional data / Outlier detection in high-dimensional data
  • brute-force algorithm / The brute-force algorithm
  • HilOut algorithm / The HilOut algorithm
• outliers
  • including / Including or excluding outliers, How it works...
  • excluding / Including or excluding outliers, How it works...
• overfitting / Evaluation
• ozone / Introduction

P

• p-value
  • about / Null Hypothesis Significance Testing
  • warning / A warning about p-values
• pair plots
  • creating / Creating pairs plots
  • working / How it works..., There's more...
• pairs() command / How it works...
• pairs() function / How it works...
• pairs plot
  • used, for creating correlation matrix / Correlation matrix using pairs plots, How it works...
• pair trading
  • about / Cointegration
• pairwise t-tests / Testing more than two means
• palettes
  • selecting / Choosing color combinations and palettes, There's more...
• PAM algorithm
  • about / The PAM algorithm
• par() command / How to do it..., How it works...
• parallel cloud computing
  • with MapReduce / Parallel cloud computing with MapReduce and Hadoop
  • with Hadoop / Parallel cloud computing with MapReduce and Hadoop
• parallel computing
  • about / Learning faster with parallel computing
  • execution time, measuring / Measuring execution time
  • with multicore package / Working in parallel with multicore and snow
  • with snow package / Working in parallel with multicore and snow
  • with foreach package / Taking advantage of parallel with foreach and doParallel
  • with doParallel package / Taking advantage of parallel with foreach and doParallel
• parallelization
  • using / Use parallelization
  • parallel R / Getting started with parallel R, An example of (some) substance
• parameter
  • estimating, of interest rate models / Parameter estimation of interest rate models
• parameter tuning
  • about / Tuning stock models for better performance
• parametric statistical tests / What if my assumptions are unfounded?
• partition-based clustering
  • about / Search engines and the k-means algorithm
  • characteristics / Search engines and the k-means algorithm
• pattern discovery / Types of machine learning algorithms
• patterns
  • about / An overview of associations and patterns
  • frequent patterns / Patterns and pattern discovery
• PCA
  • about / Principal-Component Analysis
• PDFs
  • fonts, selecting for / Choosing fonts for PostScripts and PDFs, How it works..., There's more
• Pearson's correlation coefficient / Correlations
• Pearson’s correlation / Correlation coefficients
• performance
  • measuring, confusion matrices used / Using confusion matrices to measure performance
• performance measures
  • about / Beyond accuracy – other measures of performance
  • kappa statistic / The kappa statistic
  • sensitivity / Sensitivity and specificity
  • specificity / Sensitivity and specificity
  • precision / Precision and recall
• performance tradeoffs
  • -visualizing / Visualizing performance trade-offs
• persp function / Base graphics using the default package
• pie() function / How it works...
• pie charts
  • creating, with clockwise-ordered slices / Making better, readable pie charts with clockwise-ordered slices, How it works...
  • labeling, with percentage values / Labeling a pie chart with percentage values for each slice, How it works...
  • legend, adding / Adding a legend to a pie chart, How it works...
• pie function / Base graphics using the default package
• plot
  • zooming / Zooming and filtering, There's more...
  • filtering / Getting ready, There's more...
• plot() command / How it works..., How it works...
• plot() function / There's more...
• plot background colors
  • setting / Setting plot background colors, How to do it..., There's more...
• plotCI function
  • about / There's more...
• plot function / Base graphics using the default package
• plotrix package
  • about / There's more...
• plotting point symbol
  • styles, selecting / Choosing plotting point symbol styles and sizes, How it works..., There's more..., See also
  • sizes, selecting / Choosing plotting point symbol styles and sizes, How it works..., There's more..., See also
• plot titles
  • colors, setting / How it works..., There's more...
• png() command / How to do it...
• points
  • colors, setting / Setting colors of points, lines, and bars, How to do it..., There's more...
• points() function / There's more...
• poisonous mushrooms
  • identifying, with rule learners / Example – identifying poisonous mushrooms with rule learners
• poisonous mushrooms example, with rule learners
  • data, collecting / Step 1 – collecting data
  • data, exploring / Step 2 – exploring and preparing the data
  • data, preparing / Step 2 – exploring and preparing the data
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
• Poisson regression
  • about / Understanding regression
• polynomial kernel / Using kernels for non-linear spaces
• polynomial regression / The circular decision boundary
• population / Populations, samples, and estimation
• position
  • managing / Evaluating the signals and managing the position
• positively skewed / Central tendency
• positive predictive value / Precision and recall
• POSIXlt class / How it works...
• posterior probability
  • about / Computing conditional probability with Bayes' theorem
• postpruning
  • about / Pruning the decision tree
• PostScripts
  • fonts, selecting for / Choosing fonts for PostScripts and PDFs, How it works..., There's more
• power / When things go wrong
• pre-pruning
  • about / Pruning the decision tree
• precision / Precision and recall
• predict function / Random forests
• predictive model / Types of machine learning algorithms
• PrefixSpan algorithm
  • about / The PrefixSpan algorithm
  • R implementation / The R implementation
• Price/Cash flow (P/CF) / Separating investment targets
• pricing formula
  • for call quanto / Pricing formula for a call quanto
• principal component analysis
  • APT, estimating with / Estimation of APT with principal component analysis
• prior probability
  • about / Computing conditional probability with Bayes' theorem
• probabilistic hierarchical clustering algorithm
  • about / The probabilistic hierarchical clustering algorithm
• probability
  • about / Understanding probability
• probability density function (PDF) / Probability distributions
• probability distributions
  • about / Probability distributions
  • bandwidth, selecting / Probability distributions
• probability mass function (PMF) / Probability distributions
• probability of default (PD) / Minimum capital requirements
• process, data mining
  • CRISP-DM / The data mining process, CRISP-DM
  • SEMMA / The data mining process, SEMMA
• proprietary files
  • about / Working with proprietary files and databases
  • Microsoft Excel files, reading / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • Microsoft Excel files, writing / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • SAS files, writing / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • SAS files, reading / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • SPSS files, reading / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • SPSS files, writing / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • Stata files, writing / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • Stata files, reading / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
• proprietary microarray
  • using / Analyzing bioinformatics data
• proximity-based methods
  • about / Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods
  • density-based outlier detection algorithm / Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods
  • distance-based outlier detection algorithm / Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods, The distance-based algorithm
  • NL algorithm / The NL algorithm
  • FindAllOutsM algorithm / The FindAllOutsM algorithm
  • FindAllOutsD algorithm / The FindAllOutsD algorithm
  • Dolphin algorithm / The Dolphin algorithm
  • R implementation / The R implementation
  • activity monitoring / Activity monitoring and the detection of mobile fraud
  • mobile fraud detection / Activity monitoring and the detection of mobile fraud
• pruning / Decision trees
• pure / Choosing the best split
• purity / Choosing the best split

Q

• Q-Q plots
  • about / Creating Quantile-Quantile plots
  • creating / Getting ready, There's more...
• qnorm() function / How it works...
• qnorm function
  • using / How did we get 1.96?
• qplot() function / How it works...
• qplot (qplot) / Visualization methods
• qqline() function / How it works...
• qqline function / Base graphics using the default package
• qqnorm function / Base graphics using the default package
• qqplot function / Base graphics using the default package
• qsec / Introduction
• quadratic optimization / The case of linearly separable data
• quantile / How did we get 1.96?
• quantile-quantile plot (QQ-plot)
  • using / What if my assumptions are unfounded?
• quantiles / Measuring spread – quartiles and the five-number summary
• quantmod package / Getting ready
  • URL / There's more...
• quanto options
  • about / Quanto options
  • pricing formula, for call quanto / Pricing formula for a call quanto
  • call quanto, pricing in R / Pricing a call quanto in R
• queries
  • keyword query / Information retrieval and text mining
  • boolean query / Information retrieval and text mining
  • phrase query / Information retrieval and text mining
  • proximity query / Information retrieval and text mining
  • full document query / Information retrieval and text mining
  • natural language questions / Information retrieval and text mining
• question answering (QA) system
  • about / The question answering system

R

• 1 R algorithm / The 1R algorithm
• R
  • about / Navigating the basics, Why R?, Machine learning with R
  • arithmetic operators / Arithmetic and assignment
  • assignment operators / Arithmetic and assignment
  • logical data type / Logicals and characters
  • character data type / Logicals and characters
  • flow of control constructs / Flow of control
  • help, obtaining / Getting help in R
  • data, loading / Loading data into R, Working with packages
  • k-NN, using / Using k-NN in R
  • logistic regression / Using logistic regression in R
  • overview / Introduction
  • on Linux, URL / Introduction
  • on Mac OS X, URL / Introduction
  • on Widows, URL / Introduction
  • inbuilt datasets / A note on R's built-in datasets
  • recycling in / There's more...
  • advantage / Why R?
  • disadvantage / What are the disadvantages of R?
  • statistics / Statistics and R
  • visualization / Visualization with R
  • packages, installing / Installing R packages
  • packages, loading / Loading and unloading R packages
  • packages, unloading / Loading and unloading R packages
  • data structures / R data structures
  • used, for managing data / Managing data with R
  • working with classification prediction data / Working with classification prediction data in R
• R, performance improvement
  • about / Improving the performance of R
  • large datasets, managing / Managing very large datasets
  • parallel computing / Learning faster with parallel computing
  • GPU, computing / GPU computing
  • optimized learning algorithms, deploying / Deploying optimized learning algorithms
• R-squared value / Step 4 – evaluating model performance
• Radial Basis Function (RBF) network
  • about / Activation functions
• random forests
  • about / Random forests, Random forests
  • URL / Random forests
  • strengths / Random forests
  • training / Training random forests
  • performance, evaluating / Evaluating random forest performance
  • building, with bigrf package / Growing bigger and faster random forests with bigrf
• Random forests algorithm
  • about / The Random forests algorithm
• rank
  • assigning / Correlation coefficients
• rbinom() / Introduction
• rbinom() function / How it works...
• R code
  • about / Be smart about your code, Be smarter about your code
  • memory, allocation / Allocation of memory
  • vectorization / Vectorization
• RColorBrewer
  • about / There's more...
• Rcpp
  • using / Using Rcpp
• RCurl
  • URL / Downloading the complete text of web pages
• Read-Evaluate-Print-Loop (REPL) / Navigating the basics
• rea under the ROC curve (AUC) / ROC curves
• Receiver Operating Characteristic (ROC) curve
  • about / ROC curves
  • creating / ROC curves
• recommendation systems
  • about / Recommendation systems
• recovery rate (RR) / Credit risk
• recurrent network
  • about / The direction of information travel
• recursive partitioning
  • about / Divide and conquer
• recursive splitting / Decision trees
• recycling
  • in R / There's more...
• regional maps
  • graphs, creating with / Creating graphs with regional maps, How it works..., There's more
• regression / Correlation coefficients
  • about / Understanding regression
  • simple linear regression / Simple linear regression
  • ordinary least squares estimation / Ordinary least squares estimation
  • correlation / Correlations
  • multiple linear regression / Multiple linear regression
  • adding, to trees / Adding regression to trees
• regression analysis
  • use cases / Understanding regression
• regression equations
  • about / Understanding regression
• regression models
  • building, with biglm package / Building bigger regression models with biglm
• regression plane
  • three-dimensional scatter plots with / Three-dimensional scatter plots with a regression plane, How to do it…, How it works…
• regression trees
  • about / Understanding regression trees and model trees
• regular expressions / Regular expressions
• regularization / Advanced topics
• relational database
  • about / Relational Databases
• relationships
  • exploring, between variables / Exploring relationships between variables
  • visualizing / Visualizing relationships – scatterplots
  • examining / Examining relationships – two-way cross-tabulations
• Relative Closeness (RC), chameleon algorithm
  • about / The chameleon algorithm
• Relative Interconnectivity (RI), chameleon algorithm
  • about / The chameleon algorithm
• relative strength indicator (RSI) / Built-in indicators
• relative transaction costs
  • optimal hedging / Optimal hedging in the case of relative transaction costs
• Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm / The RIPPER algorithm
• residuals / Ordinary least squares estimation
• Residual Sum of Squares (RSS) / Simple linear regression
• resubstitution error / Estimating future performance
• results
  • communicating / Communicating results
  • interpreting / Interpreting the results, Possible interpretations and suggestions
• Revolution Analytics
  • URL / Taking advantage of parallel with foreach and doParallel
• RGB (Red Green Blue) model / There's more
• rggobi package
  • about / There's more...
  • URL / There's more...
• RGL
  • about / How it works...
• rgl.surface() function
  • about / How it works...
• RHadoop
  • about / Big data
  • URL / Parallel cloud computing with MapReduce and Hadoop
• RHIPE package
  • URL / Parallel cloud computing with MapReduce and Hadoop
• right-tailed / Central tendency
• R implementation
  • using / Using another R implementation
• rio package
  • URL / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • about / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
• RIPPER algorithm
  • about / The RIPPER algorithm, The RIPPER algorithm
  • pseudocode / The RIPPER algorithm
• risk-weighted assets (RWA) / Basel I
• risk categories
  • about / Risk categories
  • market risk / Market risk
  • credit risk / Credit risk
  • operational risk / Operational risk
• risk measures
  • about / Risk measures
  • monotonicity / Risk measures
  • sub-additivity / Risk measures
  • positive homogeneity / Risk measures
  • translation invariance / Risk measures
  • analytical VaR / Analytical VaR
  • historical VaR / Historical VaR
  • Monte-Carlo simulation / Monte-Carlo simulation
• risky bank loans
  • identifying, C5.0 decision trees used / Example – identifying risky bank loans using C5.0 decision trees
• rnorm() / Introduction
• rnorm(1000) function / How it works...
• rnorm function / Estimating means
• Root Mean Squared Error (RMSE) / Simple linear regression
• rote learning
  • about / Why is the k-NN algorithm lazy?
• route outlier detection (ROD) algorithm
  • about / The route outlier detection (ROD) algorithm
  • R implementation / The R implementation
• rpart.plot
  • URL / Visualizing decision trees
• R projects
  • about / R projects
• R Scripting
  • about / R Scripting
  • RStudio / RStudio
  • running / Running R scripts
• R scripts
  • running / Running R scripts
  • example / An example script
• RSI / RSI
• RStudio
  • about / RStudio
• rudimentary ANNs / Understanding neural networks
• rug() function / How to do it...
  • about / How it works...
• rule-based classification
  • about / Rule-based classification of player types in computer games and rule-based classification, Rule-based classification
  • decision tree, transforming into decision rules / Transformation from decision tree to decision rules
  • sequential covering algorithm / Sequential covering algorithm
  • RIPPER algorithm / The RIPPER algorithm
  • R implementation / The R implementation
  • player types, classifying in computer games / Rule-based classification of player types in computer games
• rules
  • association rules / Relationship or rules discovery, Association rules
  • correlation rules / Relationship or rules discovery, Correlation rules
  • generating, from sequential patterns / Rule generation from sequential patterns
• runif() / Introduction
• rvest package
  • about / Scraping data from web pages

S

• samples / Populations, samples, and estimation
• sampling distribution / The sampling distribution
• scale() function / How it works...
• scale argument / There's more
• scatter plot / Creating a conditional scatter plot
  • layered plot, creating with / Creating a layered plot with a scatter plot and fitted line, How it works…
  • about / Three-dimensional scatter plots
• scatterplot / The relationship between two continuous variables
  • about / Visualizing relationships – scatterplots
• scatterplot3d() function / How it works..., There's more...
• scatterplot3d function / How it works…, How it works…
• scatterplot matrix (SPLOM) / Visualizing relationships among features – the scatterplot matrix
• scatter plots
  • creating / Creating basic scatter plots, How to do it...
  • working / How it works...
  • about / Introduction
  • data points, grouping / Grouping data points within a scatter plot, How it works..., There's more..., See also
  • mpg, plotting against disp / How it works...
  • creating, with smoothed density representation / Creating scatter plots with a smoothed density representation, How it works..., There's more...
• scientific notations
  • adding / Adding mathematical and scientific notations (typesetting), How to do it..., How it works..., There's more
• Scoville scale / Preparing data for use with k-NN
• scripting
  • and reproducibility / Scripting and reproducibility
• search engine
  • web page clustering / Search engine and web page clustering
• seasonal component / The seasonal component
• segmentation analysis / Types of machine learning algorithms
• select argument / There's more…
• semi-supervised learning / Clustering as a machine learning task
• SEMMA
  • about / The data mining process, SEMMA
  • sample / SEMMA
  • explore / SEMMA
  • modify / SEMMA
  • model / SEMMA
  • assess / SEMMA
• sentential frequent itemsets
  • used, for text classification / Text classification using sentential frequent itemsets
• separate and conquer
  • about / Separate and conquer
• sequence dataset
  • mining / Mining sequence dataset
  • about / Sequence dataset
  • mining, with GSP algorithm / The GSP algorithm
• sequence patterns
  • mining / Mining sequence patterns in transactional databases
  • PrefixSpan algorithm / The PrefixSpan algorithm
• sequential covering algorithm
  • about / Sequential covering algorithm
  • pseudocode / Sequential covering algorithm
• sequential patterns
  • rules, generating / Rule generation from sequential patterns
• SETAR model
  • estimating / SETAR estimation and forecasting
  • forecasting / SETAR estimation and forecasting
• shapefiles / There's more
• Shapiro-Wilk test / What if my assumptions are unfounded?
• shingling algorithm
  • about / Social network mining
• sigmoid kernel / Using kernels for non-linear spaces
• signals
  • evaluating / Evaluating the signals and managing the position
• simple linear regression
  • about / Simple linear regression, Simple linear regression
  • with binary predictor / Simple linear regression with a binary predictor
  • warning / A word of warning
  / Understanding regression
• simple moving average (SMA) / Built-in indicators
• simple tuned model
  • creating / Creating a simple tuned model
• Simpson’s Paradox / Relationships between two categorical variables
• simulation method
  • about / The simulation method
  • simulation / The simulation
  • implementation, in R / Implementation in R
  • results / Results
• simulations, in R
  • about / How R can help a lot
• single-pass-any-time clustering algorithm
  • about / The single-pass-any-time clustering algorithm
• Site4 / How it works...
• skewness degree / Central tendency
• slack variable / The case of nonlinearly separable data
• slope
  • about / Understanding regression
• slope-intercept form
  • about / Understanding regression
• SMA / SMA and EMA
• smaller samples / Smaller samples
• SMFI5 package
  • using / Using the SMFI5 package
• smoothed density scatter plots
  • creating / Creating scatter plots with a smoothed density representation, How it works..., There's more...
• smoothScatter() function / Getting ready, How it works..., There's more...
• SMS Spam Collection
  • URL / Step 1 – collecting data
• snowball
  • URL / Data preparation – cleaning and standardizing text data
• snow package
  • using / Working in parallel with multicore and snow
  • URL / Working in parallel with multicore and snow
• social network
  • mining / Social network mining
  • characteristics / Social network
  • telephone networks / Social network
  • e-mail networks / Social network
  • collaboration networks / Social network
  • example / Social network
• social networking service (SNS)
  • about / Social networking service (SNS)
  / Example – finding teen market segments using k-means clustering
• social network mining
  • about / Social network mining
  • community detection / Social network mining
  • shingling algorithm / Social network mining
  • node classification / The node classification and iterative classification algorithms
  • iterative classification algorithms / The node classification and iterative classification algorithms
  • R implementation / The R implementation
• Solar.R / Introduction
• sortCol argument / There's more…
• SPADE algorithm
  • about / The SPADE algorithm
  • features / The SPADE algorithm
  • R implementation / The R implementation
• spam e-mail
  • identifying, Naïve Bayes classification used / Identify spam e-mail and Naïve Bayes classification, Identify spam e-mail
• sparklines
  • creating / Creating sparklines, How it works...
  • about / Creating sparklines
  • feature / How it works...
• sparse matrix / Data preparation – splitting text documents into words, Data preparation – creating a sparse matrix for transaction data
• Spearman’s rank coefficient (rho) / Correlation coefficients
• spectral clustering algorithm
  • about / The spectral clustering algorithm
  • pseudocode / The spectral clustering algorithm
  • R implementation / The R implementation
• split() function / How it works...
• split point / Decision trees
• spread
  • measuring / Spread
• sprintf() function / How it works...
• SQL databases
  • data, querying in / Querying data in SQL databases
• SQL query / Why didn't we just do that in SQL?
• squared error-based clustering algorithm
  • about / Search engines and the k-means algorithm
• squashing functions / Activation functions
• stacked bar charts
  • creating / Creating stacked bar charts, How it works..., There's more..., Creating stacked bar charts, How it works…, There's more…
• stacking
  • about / Understanding ensembles
• standard deviation / Spread
  • about / Measuring spread – variance and standard deviation
• standard deviation reduction (SDR) / Adding regression to trees
• standard error / The sampling distribution
• Standard GARCH model / The standard GARCH model
• Standardized Approach (STA) / Minimum capital requirements
• static delta hedge / Static delta hedge
• static replication, of non-maturity deposits / Static replication of non-maturity deposits
• statistical arbitrage
  • about / Cointegration
• statistical hypothesis testing / Understanding regression
• statistical method
  • about / Credit card fraud detection and statistical methods
  • likelihood-based outlier detection algorithm / The likelihood-based outlier detection algorithm
  • credit card fraud detection / Credit card fraud detection
• statistics
  • about / Statistics
  • data mining / Statistics and data mining
  • machine learning / Statistics and machine learning
  • and R / Statistics and R
  • limitations, on data mining / The limitations of statistics on data mining
• STING algorithm
  • about / Recommendation system and STING
  • characteristics / Recommendation system and STING
  • pseudocode / The STING algorithm
  • R implementation / The R implementation
  • recommendation systems / Recommendation systems
• stochastic volatility (SV) models / Volatility modeling
• stock charts
  • creating / Creating stock charts, How it works..., There's more...
• stock market data
  • about / Stock market data
• stock models
  • tuning, for better performance / Tuning stock models for better performance
• stocks
  • about / Markets
• STREAM algorithm
  • about / The credit card transaction flow and STREAM algorithm
  • pseudocode / The STREAM algorithm
  • R implementation / The R implementation
  • credit card transaction flow / The credit card transaction flow
• stream data
  • mining / The credit card transaction flow and STREAM algorithm
• strptime() function / How it works...
• Structural Clustering Algorithm for Network (SCAN) algorithm
  • about / SNS and clustering graph and network data
  • pseudocode / The SCAN algorithm
  • R implementation / The R implementation
  • social networking service (SNS) / Social networking service (SNS)
• structured products
  • exotic options, embedded in / Exotic options embedded in structured products
• Structured Query Language (SQL)
  • about / Querying data in SQL databases
• subsetting / Subsetting
• subtree raising / Pruning the decision tree
• subtree replacement / Pruning the decision tree
• summarization
  • about / Summarization
• summary statistics / Exploring numeric variables
• supervised learning / Types of machine learning algorithms
• supervisory review / Supervisory review
• Supervisory Review Evaluation Process (SREP) / Supervisory review
• Support Vector Machine (SVM)
  • about / Understanding Support Vector Machines
  • applications / Understanding Support Vector Machines
  • classifications, with hyperplanes / Classification with hyperplanes
  • case of linearly separable data / The case of linearly separable data
  • case of nonlinearly separable data / The case of nonlinearly separable data
  • OCR, performing with / Example – performing OCR with SVMs
  / Bagging
• support vectors / Classification with hyperplanes
• SURFING algorithm
  • pseudocode / The SURFING algorithm
  • about / The SURFING algorithm
  • R implementation / The R implementation
• SVD
  • about / Singular-value decomposition
• SVM algorithm
  • about / Document retrieval and Support Vector Machine
  • pseudocode / The SVM algorithm
  • R implementation / The R implementation
  • parallel version, with MapReduce / Parallel version with MapReduce
  • used, for document retrieval / Document retrieval
• SVMlight
  • about / Step 3 – training a model on the data
  • URL / Step 3 – training a model on the data
• symbolic sequences
  • mining / Web click streams and mining symbolic sequences
• synapse
  • about / From biological to artificial neurons
• systemic risk, in nutshell
  • about / Systemic risk in a nutshell

T

• t-distribution (Student’s t-distribution)
  • about / Smaller samples
• t-test / Testing the mean of one sample
• TA
  • about / Technical analysis
  • rules / Technical analysis
  • toolkit / The TA toolkit
• TA, tools
  • about / The TA toolkit
  • support-resistance / The TA toolkit
  • price channels / The TA toolkit
  • chart patterns / The TA toolkit
  • candle patterns / The TA toolkit, Candle patterns: key reversal
  • indicators / The TA toolkit
  • markets / Markets
  • charts, plotting / Plotting charts - bitcoin
  • built-in indicators / Built-in indicators
  • SMA / SMA and EMA
  • EMA / SMA and EMA
  • RSI / RSI
  • MACD / MACD
  • signals, evaluating / Evaluating the signals and managing the position
  • position, managing / Evaluating the signals and managing the position
• Tab-Separated Value (TSV)
  • about / Importing and saving data from CSV files
• tabular
  • about / Importing and saving data from CSV files
• tabular data structures
  • generalizing, with dplyr package / Generalizing tabular data structures with dplyr
• teen market segments search, with k-means clustering
  • about / Example – finding teen market segments using k-means clustering
  • data, collecting / Step 1 – collecting data
  • data, exploring / Step 2 – exploring and preparing the data
  • data, preparing / Step 2 – exploring and preparing the data, Data preparation – dummy coding missing values, Data preparation – imputing the missing values
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
• temp / Introduction
• Term Frequency-Inverse Document Frequency (TF-IDF)
  • about / Search engine and web page clustering
• terminal nodes / Understanding decision trees
• terrain.colors() function / How it works...
• test statistic
  • defining / Null Hypothesis Significance Testing
• text() function / How it works..., How it works..., How it works...
• text classification
  • with sentential frequent itemsets / Text classification using sentential frequent itemsets
• text descriptions
  • adding, to graphs / Adding text descriptions to graphs, How to do it..., How it works..., There's more
• text elements
  • colors, setting / Setting colors for text elements – axis annotations, labels, plot titles, and legends
• text mining
  • about / Text mining, Text mining and TM packages
  • IR / Information retrieval and text mining
  • for prediction / Mining text for prediction
• Text Retrieval Conference (TREC)
  • about / Identify spam e-mail
• text summarization
  • about / Text summarization
  • topic representation / Topic representation
  • multidocument summarization algorithm / The multidocument summarization algorithm
  • Maximal Marginal Relevance (MMR) algorithm / The Maximal Marginal Relevance algorithm
• TGARCH model / The Threshold GARCH model (TGARCH)
• themeplot() function / How it works...
• three-dimensional bar charts
  • about / Three-dimensional bar charts, How it works…
• three-dimensional density plots
  • about / Three-dimensional density plots, How to do it..., How it works…
• three-dimensional scatter plots
  • creating / Creating three-dimensional scatter plots, How it works..., There's more...
  • about / Three-dimensional scatter plots, How it works…, There's more…
  • with regression plane / Three-dimensional scatter plots with a regression plane, How to do it…, How it works…
• three-dimensional surface plots
  • creating / Creating three-dimensional surface plots, How it works...
• three-sigma rule / The three-sigma rule and using z-tables
• threshold activation function / Activation functions
• tick marks
  • adjusting / Adjusting axis annotations and tick marks, How to do it..., How it works..., There's more...
• tidyr / tidyr
• time-series data
  • mining / Predicting future prices and time-series analysis
  • clustering / Stock market data and time-series clustering and classification
  • clustering, with hError algorithm / The hError algorithm
  • clustering, with 1NN classifier algorithm / Time-series classification with the 1NN classifier
  • stock market data / Stock market data
• time axis labels
  • formatting / Annotating axis labels in different human-readable time formats, How it works...
• time series charts
  • about / Formatting time series data for plotting
• time series data
  • formatting / Formatting time series data for plotting, How it works...
  • plotting, by averaging over different time periods / Plotting data with varying time-averaging periods, How it works...
  • visualizing, as calendar heat maps / Visualizing time series as calendar heat maps, How to do it..., How it works..., There's more
• Time To Live (TTL)
  • about / Trojan traffic identification method
• time variable
  • plotting, on X axis / Plotting the date or time variable on the x axis, How to do it..., How it works...
• titles
  • fonts, setting / Setting fonts for annotations and titles, How it works..., See also
• topic detection
  • about / Detecting novelty in text and topic detection
• topic representation
  • about / Topic representation
• topic signature
  • about / Topic representation
• Tracking Evolving Clusters in NOisy Streams (TECNO-STREAMS) algorithm
  • about / Web click streams and mining symbolic sequences, The TECNO-STREAMS algorithm
  • R implementation / The R implementation
  • used, for mining web click streams / Web click streams
• training / Abstraction
• transaction costs
  • hedging / Hedging in the presence of transaction costs
  • hedge optimization / Optimization of the hedge
  • optimal hedging, of absolute transaction costs / Optimal hedging in the case of absolute transaction costs
  • optimal hedging, of relative transaction costs / Optimal hedging in the case of relative transaction costs
• tree pruning
  • about / Tree pruning
  • post-pruning / Tree pruning
  • pre-pruning / Tree pruning
• trees
  • regression, adding to / Adding regression to trees
• tree structure
  • about / Understanding decision trees
• Trellis graphs
  • lattice used / Trellis graphs using lattice
• trend line / Correlation coefficients
• Trojan horse
  • about / Trojan traffic identification method
• Trojan traffic identification
  • with Bayes classification / Trojan traffic identification method and Bayes classification, Trojan traffic identification method
• tseries package
  • about / Getting ready
• Tukey’s variation / Relationships between a categorical and a continuous variable
• tuning parameters / Decision trees
• tuning process
  • customizing / Customizing the tuning process
• two-dimensional Wiener processes / Two-dimensional Wiener processes
• two-way cross-tabulation
  • about / Examining relationships – two-way cross-tabulations
• Type I errors / When things go wrong
• Type II errors / When things go wrong

U

• UCI Machine Learning Data Repository
  • URL / Step 1 – collecting data, Step 1 – collecting data, Step 1 – collecting data
  • about / Step 1 – collecting data
• UCI Machine Learning Repository
  • about / Data source
  • URL / Data source
• undirected graphs
  • about / Graph
• unimodal / Measuring the central tendency – the mode
• unit of analysis / Types of input data
• unit of observation / Types of input data
• unit step activation function / Activation functions
• univariate data
  • about / Univariate data
• univariate statistics
  • about / Exploring relationships between variables
• universal function approximator / The number of nodes in each layer
• universally consistent / Logoptimal portfolios
• unsanitized data
  • analysis / Analysis with unsanitized data
• unsupervised image categorization
  • with affinity propagation (AP) clustering / Unsupervised image categorization
• unsupervised learning / Types of machine learning algorithms
• user search intent
  • determining / The user search intent

V

• Value at Risk (VaR) / Minimum capital requirements
• VAR
  • about / Vector autoregressive models
  • implementing / VAR implementation example
  • and VECM, cointegrated / Cointegrated VAR and VECM
• variable
  • functions, plotting / Plotting functions of a variable in a dataset, How it works..., There's more...
• Vasicek model
  • about / The Vasicek model
• VECM
  • and VAR, cointegrated / Cointegrated VAR and VECM
• vector
  • about / Vectors
• vector-space model
  • about / Search engine and web page clustering
• vector formats
  • graphs, exporting in / Exporting graphs in vector formats – SVG, PDF, and PS, How it works..., There's more
• vectorized functions / Vectorized functions
• vectors
  • about / Vectors
  • building / Vectors
  • subsetting / Subsetting
  • vectorized functions / Vectorized functions
  • advanced subsetting / Advanced subsetting
  • recycling / Recycling
• vector types
  • types / Vectors
• version control
  • about / Version control
• vertical decision boundary / The vertical decision boundary
• vertical error bars
  • bar charts, creating with / Creating bar charts with vertical error bars, How to do it..., How it works..., There's more...
• vertical format
  • about / Input data characteristics and data structure
• vertical grid lines
  • adding / Adding horizontal and vertical grid lines, How it works..., There's more..., See also
• vertical markers
  • adding, abline() function used / Adding vertical markers to indicate specific time events, How it works..., There's more...
• visitor analysis, in browser cache
  • hit / Visitor analysis in the browser cache
  • unique visitors / Visitor analysis in the browser cache
  • new/return visitors / Visitor analysis in the browser cache
  • page views / Visitor analysis in the browser cache
  • page views per visitor / Visitor analysis in the browser cache
  • IP address / Visitor analysis in the browser cache
  • visitor location / Visitor analysis in the browser cache
  • visitor language / Visitor analysis in the browser cache
  • referring pages/sites (URLs) / Visitor analysis in the browser cache
  • keywords / Visitor analysis in the browser cache
  • browser type / Visitor analysis in the browser cache
  • operating system version / Visitor analysis in the browser cache
  • screen resolution / Visitor analysis in the browser cache
  • Java or Flash-enabled / Visitor analysis in the browser cache
  • connection speed / Visitor analysis in the browser cache
  • errors / Visitor analysis in the browser cache
  • visit duration / Visitor analysis in the browser cache
  • visitor paths/navigation / Visitor analysis in the browser cache
  • bounce rate / Visitor analysis in the browser cache
• visualization
  • about / Visualization of results
  • with R / Visualization with R
• visualization, features
  • novel / Visualization of results
  • informative / Visualization of results
  • efficient / Visualization of results
  • aesthetic / Visualization of results
• visualization methods
  • about / Visualization methods
  • of categorical data / Categorical and continuous variables
  • of continuous variables / Categorical and continuous variables
  • of two categorical data / Two categorical variables
  • of two continuous variables / Two continuous variables
  • of multiple continuous variables / More than two continuous variables
• Visualizing Categorical Data (VSD) / Two categorical variables
• volatility modeling
  • about / Volatility modeling
  • GARCH modeling, with rugarch package / GARCH modeling with the rugarch package
  • simulation / Simulation and forecasting
  • forecasting / Simulation and forecasting
• volatility modeling, empirical observations
  • volatility clustering / Volatility modeling
  • non-normality of asset returns / Volatility modeling
  • leverage effect / Volatility modeling
• volume
  • about / Motivation
• volume forecasting model
  • about / The volume forecasting model
• volume weighted average price (VWAP) / Motivation
• Voronoi diagram / Using distance to assign and update clusters
• vs / Introduction

W

• WAVE clustering algorithm
  • about / Opinion mining and WAVE clustering
  • characteristics / Opinion mining and WAVE clustering
  • pseudocode / The WAVE cluster algorithm
  • R implementation / The R implementation
  • opinion mining / Opinion mining
• web attack
  • detecting, ID3 algorithm used / Web attack detection
  • DOS / Web attack detection
  • R2L / Web attack detection
  • U2R / Web attack detection
  • probing / Web attack detection
• web click streams
  • mining / Web click streams and mining symbolic sequences, Web click streams
• web data mining
  • about / Web data mining
  • web structure mining / Web data mining
  • web content mining / Web data mining
  • web usage mining / Web data mining
• web data mining, tasks
  • information extraction (IE) / Web data mining
  • natural language processing (NLP) / Web data mining
  • question answering / Web data mining
  • resource discovery / Web data mining
• web key resource page judgment
  • with CART algorithm / Web key resource page judgment using CART, Web key resource page judgment
  • attributes / Web key resource page judgment
• web logs
  • used, for web usage mining / Web usage mining with web logs
• web page clustering
  • about / Search engine and web page clustering
• web pages
  • clustering / Clustering web pages
  • genre categorization / Genre categorization of web pages
  • complete text, downloading / Downloading the complete text of web pages
  • data, parsing / Scraping data from web pages
  • XML documents, parsing / Parsing XML documents
  • JSON, parsing from web APIs / Parsing JSON from web APIs
• web scraping
  • about / Scraping data from web pages
• web sentiment analysis
  • about / Web sentiment analysis
• web server
  • performance, monitoring / Monitoring the performance of the web server
• web spam
  • detecting, C4.5 algorithm used / Web spam detection using C4.5, Web spam detection
  • link spam / Web spam detection
  • content spam / Web spam detection
  • cloaking / Web spam detection
• web usage mining
  • with web logs / Web usage mining with web logs
  • with, FCA-based association rule mining algorithm / The FCA-based association rule mining algorithm
• whiskers, box plots
  • adjusting / Adjusting the extent of plot whiskers outside the box, How to do it..., How it works...
• width
  • selecting / Choosing line styles and width, How to do it..., How it works...
• Wilcoxon rank-sum test / What if my assumptions are unfounded?
• wind / Introduction
• Windows
  • URL / Introduction
  • font families, selecting under / Getting ready, How it works..., There's more
• wine quality estimation, with regression trees
  • about / Example – estimating the quality of wines with regression trees and model trees
  • data, collecting / Step 1 – collecting data
  • data, preparing / Step 2 – exploring and preparing the data
  • data, exploring / Step 2 – exploring and preparing the data
  • model, training on data / Step 3 – training a model on the data
  • decision trees, visualizing / Visualizing decision trees
  • model performance, evaluating / Step 4 – evaluating model performance
  • performance, measuring with mean absolute error / Measuring performance with the mean absolute error
  • model performance, improving / Step 5 – improving model performance
• within cluster sum of squares (WCSS) / Big data K-means clustering analysis
• word cloud
  • about / Visualizing text data – word clouds
• wordcloud package
  • URL / Visualizing text data – word clouds
• WordNet
  • URL / Data source
• world map
  • global data plotting, by countries / Getting ready, How it works..., See also
• wt / Introduction

X

• x ayes limits
  • adjusting / Adjusting x and y axes' limits, How it works..., There's more...
• x bar / Central tendency
• XML
  • about / XML
• xml2 GitHub
  • URL / Parsing XML documents
• XML documents
  • parsing / Parsing XML documents
• XML package
  • about / Parsing XML documents
  • URL / Parsing XML documents
• XPath
  • URL / XML
• xyplot() command / How it works...

Y

• y ayes limits
  • adjusting / Adjusting x and y axes' limits, How it works..., There's more..., See also

Z

• z-score / Preparing data for use with k-NN
• z-scores / Correlation coefficients
• z-score standardization / Preparing data for use with k-NN, Transformation – z-score standardization
• z-tables
  • using / The three-sigma rule and using z-tables
• ZeroR / The 1R algorithm
• zoo() function / There's more...
• zooming
  • plot / Zooming and filtering, There's more...