1. Warming Up
by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs,
R: Data Analysis and Visualization
- R: Data Analysis and Visualization
- Table of Contents
- R: Data Analysis and Visualization
- I. Module 1: Data Analysis with R
- 1. RefresheR
- 2. The Shape of Data
- 3. Describing Relationships
- 4. Probability
- 5. Using Data to Reason About the World
- 6. Testing Hypotheses
- 7. Bayesian Methods
- 8. Predicting Continuous Variables
- 9. Predicting Categorical Variables
- 10. Sources of Data
- 11. Dealing with Messy Data
- 12. Dealing with Large Data
- 13. Reproducibility and Best Practices
- II. Module 2: R Graphs
- 1. R Graphics
- 2. Basic Graph Functions
- Introduction
- Creating basic scatter plots
- Creating line graphs
- Creating bar charts
- Creating histograms and density plots
- Creating box plots
- Adjusting x and y axes' limits
- Creating heat maps
- Creating pairs plots
- Creating multiple plot matrix layouts
- Adding and formatting legends
- Creating graphs with maps
- Saving and exporting graphs
- 3. Beyond the Basics – Adjusting Key Parameters
- Introduction
- Setting colors of points, lines, and bars
- Setting plot background colors
- Setting colors for text elements – axis annotations, labels, plot titles, and legends
- Choosing color combinations and palettes
- Setting fonts for annotations and titles
- Choosing plotting point symbol styles and sizes
- Choosing line styles and width
- Choosing box styles
- Adjusting axis annotations and tick marks
- Formatting log axes
- Setting graph margins and dimensions
- 4. Creating Scatter Plots
- Introduction
- Grouping data points within a scatter plot
- Highlighting grouped data points by size and symbol type
- Labeling data points
- Correlation matrix using pairs plots
- Adding error bars
- Using jitter to distinguish closely packed data points
- Adding linear model lines
- Adding nonlinear model curves
- Adding nonparametric model curves with lowess
- Creating three-dimensional scatter plots
- Creating Quantile-Quantile plots
- Displaying the data density on axes
- Creating scatter plots with a smoothed density representation
- 5. Creating Line Graphs and Time Series Charts
- Introduction
- Adding customized legends for multiple-line graphs
- Using margin labels instead of legends for multiple-line graphs
- Adding horizontal and vertical grid lines
- Adding marker lines at specific x and y values using abline
- Creating sparklines
- Plotting functions of a variable in a dataset
- Formatting time series data for plotting
- Plotting the date or time variable on the x axis
- Annotating axis labels in different human-readable time formats
- Adding vertical markers to indicate specific time events
- Plotting data with varying time-averaging periods
- Creating stock charts
- 6. Creating Bar, Dot, and Pie Charts
- Introduction
- Creating bar charts with more than one factor variable
- Creating stacked bar charts
- Adjusting the orientation of bars – horizontal and vertical
- Adjusting bar widths, spacing, colors, and borders
- Displaying values on top of or next to the bars
- Placing labels inside bars
- Creating bar charts with vertical error bars
- Modifying dot charts by grouping variables
- Making better, readable pie charts with clockwise-ordered slices
- Labeling a pie chart with percentage values for each slice
- Adding a legend to a pie chart
- 7. Creating Histograms
- Introduction
- Visualizing distributions as count frequencies or probability densities
- Setting the bin size and the number of breaks
- Adjusting histogram styles – bar colors, borders, and axes
- Overlaying a density line over a histogram
- Multiple histograms along the diagonal of a pairs plot
- Histograms in the margins of line and scatter plots
- 8. Box and Whisker Plots
- Introduction
- Creating box plots with narrow boxes for a small number of variables
- Grouping over a variable
- Varying box widths by the number of observations
- Creating box plots with notches
- Including or excluding outliers
- Creating horizontal box plots
- Changing the box styling
- Adjusting the extent of plot whiskers outside the box
- Showing the number of observations
- Splitting a variable at arbitrary values into subsets
- 9. Creating Heat Maps and Contour Plots
- 10. Creating Maps
- 11. Data Visualization Using Lattice
- Introduction
- Creating bar charts
- Creating stacked bar charts
- Creating bar charts to visualize cross-tabulation
- Creating a conditional histogram
- Visualizing distributions through a kernel-density plot
- Creating a normal Q-Q plot
- Visualizing an empirical Cumulative Distribution Function
- Creating a boxplot
- Creating a conditional scatter plot
- 12. Data Visualization Using ggplot2
- 13. Inspecting Large Datasets
- 14. Three-dimensional Visualizations
- 15. Finalizing Graphs for Publications and Presentations
- Introduction
- Exporting graphs in high-resolution image formats – PNG, JPEG, BMP, and TIFF
- Exporting graphs in vector formats – SVG, PDF, and PS
- Adding mathematical and scientific notations (typesetting)
- Adding text descriptions to graphs
- Using graph templates
- Choosing font families and styles under Windows, Mac OS X, and Linux
- Choosing fonts for PostScripts and PDFs
- III. Module 3: Learning Data Mining with R
- 1. Warming Up
- 2. Mining Frequent Patterns, Associations, and Correlations
- An overview of associations and patterns
- Market basket analysis
- Hybrid association rules mining
- Mining sequence dataset
- The R implementation
- High-performance algorithms
- 3. Classification
- Classification
- Generic decision tree induction
- High-value credit card customers classification using ID3
- Web spam detection using C4.5
- Web key resource page judgment using CART
- Trojan traffic identification method and Bayes classification
- Identify spam e-mail and Naïve Bayes classification
- Rule-based classification of player types in computer games and rule-based classification
- 4. Advanced Classification
- 5. Cluster Analysis
- 6. Advanced Cluster Analysis
- Customer categorization analysis of e-commerce and DBSCAN
- Clustering web pages and OPTICS
- Visitor analysis in the browser cache and DENCLUE
- Recommendation system and STING
- Web sentiment analysis and CLIQUE
- Opinion mining and WAVE clustering
- User search intent and the EM algorithm
- Customer purchase data analysis and clustering high-dimensional data
- SNS and clustering graph and network data
- 7. Outlier Detection
- Credit card fraud detection and statistical methods
- Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods
- Intrusion detection and density-based methods
- Intrusion detection and clustering-based methods
- Monitoring the performance of the web server and classification-based methods
- Detecting novelty in text, topic detection, and mining contextual outliers
- Collective outliers on spatial data
- Outlier detection in high-dimensional data
- 8. Mining Stream, Time-series, and Sequence Data
- 9. Graph Mining and Network Analysis
- 10. Mining Text and Web Data
- IV. Module 4: Mastering R for Quantitative Finance
- 1. Time Series Analysis
- 2. Factor Models
- 3. Forecasting Volume
- 4. Big Data – Advanced Analytics
- 5. FX Derivatives
- 6. Interest Rate Derivatives and Models
- 7. Exotic Options
- A general pricing approach
- The role of dynamic hedging
- How R can help a lot
- A glance beyond vanillas
- Greeks – the link back to the vanilla world
- Pricing the Double-no-touch option
- Another way to price the Double-no-touch option
- The life of a Double-no-touch option – a simulation
- Exotic options embedded in structured products
- References
- 8. Optimal Hedging
- 9. Fundamental Analysis
- 10. Technical Analysis, Neural Networks, and Logoptimal Portfolios
- 11. Asset and Liability Management
- 12. Capital Adequacy
- 13. Systemic Risks
- V. Module 5: Machine Learning with R module
- 1. Introducing Machine Learning
- 2. Managing and Understanding Data
- R data structures
- Managing data with R
- Exploring and understanding data
- Exploring the structure of data
- Exploring numeric variables
- Measuring the central tendency – mean and median
- Measuring spread – quartiles and the five-number summary
- Visualizing numeric variables – boxplots
- Visualizing numeric variables – histograms
- Understanding numeric data – uniform and normal distributions
- Measuring spread – variance and standard deviation
- Exploring categorical variables
- Exploring relationships between variables
- 3. Lazy Learning – Classification Using Nearest Neighbors
- 4. Probabilistic Learning – Classification Using Naive Bayes
- 5. Divide and Conquer – Classification Using Decision Trees and Rules
- Understanding decision trees
- Example – identifying risky bank loans using C5.0 decision trees
- Understanding classification rules
- Example – identifying poisonous mushrooms with rule learners
- 6. Forecasting Numeric Data – Regression Methods
- Understanding regression
- Example – predicting medical expenses using linear regression
- Understanding regression trees and model trees
- Example – estimating the quality of wines with regression trees and model trees
- 7. Black Box Methods – Neural Networks and Support Vector Machines
- 8. Finding Patterns – Market Basket Analysis Using Association Rules
- 9. Finding Groups of Data – Clustering with k-means
- 10. Evaluating Model Performance
- 11. Improving Model Performance
- 12. Specialized Machine Learning Topics
- Working with proprietary files and databases
- Working with online data and services
- Working with domain-specific data
- Improving the performance of R
- Managing very large datasets
- Learning faster with parallel computing
- GPU computing
- Deploying optimized learning algorithms
- Building bigger regression models with biglm
- Growing bigger and faster random forests with bigrf
- Training and evaluating models in parallel with caret
- A. Reflect and Test Yourself Answers
- Module 1: Data Analysis with R
- Chapter 1: RefresheR
- Chapter 2: The Shape of Data
- Chapter 3: Describing Relationships
- Chapter 4: Probability
- Chapter 5: Using Data to Reason About the World
- Chapter 6: Testing Hypotheses
- Chapter 7: Bayesian Methods
- Chapter 8: Predicting Continuous Variables
- Chapter 9: Predicting Categorical Variables
- Chapter 10: Sources of Data
- Chapter 11: Dealing with Messy Data
- Chapter 12: Dealing with Large Data
- Module 2: R Graphs
- Chapter 1: R Graphics
- Chapter 2: Basic Graph Functions
- Chapter 3: Beyond the Basics – Adjusting Key Parameters
- Chapter 4: Creating Scatter Plots
- Chapter 5: Creating Line Graphs and Time Series Charts
- Chapter 6: Creating Bar, Dot, and Pie Charts
- Chapter 7: Creating Histograms
- Chapter 8: Box and Whisker Plots
- Chapter 9: Creating Heat Maps and Contour Plots
- Module 4: Mastering R for Quantitative Finance
- Module 5: Machine Learning with R
- Chapter 1: Introducing Machine Learning
- Chapter 2: Managing and Understanding Data
- Chapter 3: Lazy Learning – Classification Using Nearest Neighbors
- Chapter 4: Probabilistic Learning – Classification Using Naive Bayes
- Chapter 5: Divide and Conquer – Classification Using Decision Trees and Rules
- Chapter 6: Forecasting Numeric Data – Regression Methods
- Chapter 7: Black Box Methods – Neural Networks and Support Vector Machines
- Chapter 8: Finding Patterns – Market Basket Analysis Using Association Rules
- Module 1: Data Analysis with R
- B. Bibliography
- Index
In this chapter, you will learn basic data mining terms such as data definition, preprocessing, and so on.
The most important data mining algorithms will be illustrated with R to help you grasp the principles quickly, including but not limited to, classification, clustering, and outlier detection. Before diving right into data mining, let's have a look at the topics we'll cover:
- Data mining
- Social network mining
- Text mining
- Web data mining
- Why R
- Statistics
- Machine learning
- Data attributes and description
- Data measuring
- Data cleaning
- Data integration
- Data reduction
- Data transformation and discretization
- Visualization of results
In the history of humankind, the results of data from every aspect is extensive, for example websites, social networks by user's e-mail or name or account, search terms, locations on map, companies, IP addresses, books, films, music, and products.
Data mining techniques can be applied to any kind of old or emerging data; each data type can be best dealt with using certain, but not all, techniques. In other words, the data mining techniques are constrained by data type, size of the dataset, context of the tasks applied, and so on. Every dataset has its own appropriate data mining solutions.
New data mining techniques always need to be researched along with new data types once the old techniques cannot be applied to it or if the new data type cannot be transformed onto the traditional data types. The evolution of stream mining algorithms applied to Twitter's huge source set is one typical example. The graph mining algorithms developed for social networks is another example.
The most popular and basic forms of data are from databases, data warehouses, ordered/sequence data, graph data, text data, and so on. In other words, they are federated data, high dimensional data, longitudinal data, streaming data, web data, numeric, categorical, or text data.
Big data is large amount of data that does not fit in the memory of a single machine. In other words, the size of data itself becomes a part of the issue when studying it. Besides volume, two other major characteristics of big data are variety and velocity; these are the famous three Vs of big data. Velocity means data process rate or how fast the data is being processed. Variety denotes various data source types. Noises arise more frequently in big data source sets and affect the mining results, which require efficient data preprocessing algorithms.
As a result, distributed filesystems are used as tools for successful implementation of parallel algorithms on large amounts of data; it is a certainty that we will get even more data with each passing second. Data analytics and visualization techniques are the primary factors of the data mining tasks related to massive data. The characteristics of massive data appeal to many new data mining technique-related platforms, one of which is RHadoop. We'll be describing this in a later section.
Some data types that are important to big data are as follows:
- The data from the camera video, which includes more metadata for analysis to expedite crime investigations, enhanced retail analysis, military intelligence, and so on.
- The second data type is from embedded sensors, such as medical sensors, to monitor any potential outbreaks of virus.
- The third data type is from entertainment, information freely published through social media by anyone.
- The last data type is consumer images, aggregated from social medias, and tagging on these like images are important.
Here is a table illustrating the history of data size growth. It shows that information will be more than double every two years, changing the way researchers or companies manage and extract value through data mining techniques from data, revealing new data mining studies.
|
Year |
Data Sizes |
Comments |
|---|---|---|
|
N/A |
1 MB (Megabyte) = | |
|
N/A |
1 PB (Petabyte) = | |
|
1999 |
1 EB |
1 EB (Exabyte) = |
|
2007 |
281 EB |
The world produced about 281 Exabyte of unique information. |
|
2011 |
1.8 ZB |
1 ZB (Zetabyte)= |
|
Very soon |
1 YB(Yottabytes)= |
Efficiency, scalability, performance, optimization, and the ability to perform in real time are important issues for almost any algorithms, and it is the same for data mining. There are always necessary metrics or benchmark factors of data mining algorithms.
As the amount of data continues to grow, keeping data mining algorithms effective and scalable is necessary to effectively extract information from massive datasets in many data repositories or data streams.
The storage of data from a single machine to wide distribution, the huge size of many datasets, and the computational complexity of the data mining methods are all factors that drive the development of parallel and distributed data-intensive mining algorithms.
-
No Comment