Index

• absolute deviation 1
• absolute value 1
• abstraction levels 1
• accuracy 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47
• – estimation 1, 2, 3, 4
• – measure 1, 2
• – rate 1, 2, 3, 4
• Advanced Composition Explorer (ACE) 1
• Aggregate 1, 2, 3, 4, 5, 6
• aggregation 1
• algorithm 1, 2
• – flow chart of 1
• – repetition 1
• algorithms. See specific algorithms
• anomalies 1, 2, 3. See also outliers
• approximately 1, 2, 3, 4, 5
• Artificial Neural Network 1, 2, 3
• – Feed Forward Back Propagation 1
• – Learning Vector Quantization 1
• association analysis 1
• association rule 1
• asymmetric 1
• attribute construction 1, 2, 3, 4, 5, 6, 7
• – classification 1, 2, 3, 4, 5, 6, 7
• attribute selection measures 1, 2, 3, 4, 5
• – backward elimination 1
• – decision tree induction 1, 2
• – forward selection 1
• – gain ratio 1
• – Gini index 1, 2
• – greedy methods 1
• – information gain 1, 2
• – stepwise backward elimination 1
• – stepwise forward selection 1
• attributes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
• – Accounts Payable 1, 2
• – Accounts Receivable 1
• – After Tax ROE 1, 2
• – binary 1
• – Boolean 1
• – Capital Surplus 1, 2
• – categorical 1, 2
• – class label 1, 2
• – class label attribute 1, 2
• – continuous 1
• – correlated 1
• – discrete 1
• – generalization threshold control 1
• – grouping 1
• – interval-scaled 1
• – Nominal Attributes 1
• – Numeric Attributes 1
• – ordered 1, 2, 3, 4
• – ordinal 1, 2
• – ratio-scaled 1
• – set of 1, 2, 3, 4, 5
• – types of 1, 2
• average (or mean) 1, 2
• backpropagation 1, 2, 3, 4, 5, 6, 7
• – activation function 1, 2
• – case updating 1
• – error 1, 2
• – gradient descent 1
• – logistic (or sigmoid) function 1
• – updating 1
• bar charts 1
• Bayes bootstrap 1
• Bayes’ theorem 1, 2, 3, 4, 5. See also classification
• – algorithms 1
• – class conditional independence 1, 2
• – posterior probability 1, 2
• – prior probability 1
• Bayesian classification 1, 2, 3
• – basis 1
• biases 1, 2, 3, 4, 5
• Big Data 1, 2
• Big Data and Data Science 1
• bimodal 1
• bin boundaries 1
• binary attributes 1, 2. See also attributes
• – binary 1
• Binary SVM or Linear SVM 1, 2
• Binning methods 1
• – equal-frequency 1
• – smoothing by bin boundaries 1
• – smoothing by bin means 1
• bivariate distribution 1
• Box-Counting Dimension 1, 2
• boxplots 1, 2, 3, 4
• – computation 1
• – example 1
• Brackets or Punctuation Marks 1
• BW application of 1
• – Examples 1
• C4.5 1, 2, 3, 4, 5. See also decision tree induction
• – examples 1
• – gain ratio use 1, 2, 3
• CART 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15. See also decision tree induction
• – examples 1
• – Gini index use 1, 2, 3
• categorical (or nominal) attributes 1
• cells 1, 2
• central tendency measures 1, 2
• – for missing values 1
• – mean (or average) 1
• – median (or middle value) 1
• – mode (or most common value) 1
• challenges 1, 2
• class conditional independence 1
• class imbalance problem 1, 2
• class label attribute 1, 2
• Classification 1, 2, 3, 4, 5, 6, 7
• – accuracy 1, 2, 3, 4, 5
• – backpropagation 1
• – confusion matrix 1
• classification methods 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18
• – Bayesian classifier algorithm 1, 2, 3
• – decision tree induction 1, 2
• – general approach to 1, 2
• – IF-THEN rules 1, 2, 3, 4, 5
• – interpretability 1
• – k-nearest-neighbor 1
• – learning step 1
• – model selection 1, 2
• – multiclass 1, 2
• – neural networks 1, 2, 3
• – prediction problems 1
• – random forest 1
• – recall 1, 2
• – robustness 1
• – scalability 1
• – speed 1
• – support vector machines (SVMs) 1
• – tree pruning 1
• Classification of Data 1
• classifiers 1, 2, 3, 4, 5
• – decision tree 1
• – error rate 1, 2
• – recognition rate 1
• class-labelled training 1
• clusters 1, 2, 3, 4, 5, 6, 7
• Colour Models 1
• – BW colour 1
• – RGB colour 1
• Compiler 1
• completeness 1
• concept hierarchies 1
• conditional probability 1, 2, 3, 4, 5, 6, 7
• confidence 1
• confidence interval 1
• confusion matrix 1
• consecutive rules 1
• Constant concept 1
• Constants 1, 2
• contingency table 1
• contingency table for 1
• continuous attribute 1
• convex 1
• correlation analysis 1
• – nominal data 1
• – numeric data 1
• correlation coefficient 1, 2, 3
• Covariance 1, 2, 3
• Covariance analysis 1, 2
• cross-validation 1
• Data 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53
• – class-labelled training 1
• – database 1, 2, 3
• – for data mining 1, 2, 3, 4, 5, 6, 7, 8
• – graph 1
• – growth 1, 2, 3, 4, 5, 6
• – linearly inseparable 1
• – measures of dissimilarity 1
• – measures of similarity 1
• – multivariate 1
• – overfitting 1, 2
• – Real dataset 1, 2, 3, 4, 5, 6
• – relational 1, 2
• – sample 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
• – skewed 1, 2
• – spatial 1
• – statistical descriptions 1, 2, 3
• – Synthetic dataset 1, 2, 3, 4, 5, 6, 7
• – training 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
• – types of 1, 2, 3, 4
• – web 1
• data aggregation 1
• data analysis. See data mining
• – algorithm 1, 2
• – core objects 1
• – density estimation 1
• – density-based cluster 1
• – density-connected 1, 2, 3
• – density-reachable 1, 2, 3, 4
• – directly density-reachable 1
• – neighborhood density 1, 2
• – RDBMS 1
• data auditing tools 1
• data classification. See classification
• data cleaning. See also data preprocessing
• – discrepancy detection 1
• – missing values 1, 2
• – noisy data 1
• – outlier analysis 1
• data contents 1
• data discretization. See discretization data dispersion 1, 2, 3
• – boxplots 1
• – quartiles 1, 2
• – standard deviation 1, 2
• – variance 1
• data integration. See also data preprocessing
• – correlation analysis 1
• – entity identification problem 1
• – redundancy 1, 2
• data matrix 1, 2
• – rows and columns 1
• – two-mode matrix 1
• data migration tools 1
• data mining 1, 2, 3, 4, 5, 6, 7, 8, 9
• Data Normalization. See also data preprocessing
• – attribute construction 1, 2
• – concept hierarchy generation 1
• – discretization 1, 2
• – smoothing 1
• – strategies 1
• data objects 1, 2, 3, 4, 5
• data preprocessing 1, 2, 3, 4
• – cleaning 1, 2, 3, 4
• – integration 1, 2, 3
• – quality 1
• – reduction 1, 2, 3
• – transformation 1, 2, 3
• data quality 1
• – completeness 1
• – consistency 1
• – interpretability 1
• data reduction. See also data preprocessing
• – attribute subset selection 1
• – clustering 1
• – compression 1
• – data aggregation 1
• – numerosity 1, 2
• – parametric 1
• – sampling 1
• Data Set (or dataset) 1, 2
• – economy dataset (USA, New Zealand, Italy, Sweden (U.N.I.S.)) 1
• – medical dataset (WAIS-R dataset and MS dataset) 1
• data type 1, 2, 3, 4
• – complex 1
• – cross-validation 1
• – data visualization 1
• – for data mining 1
• – pixel 1
• databases 1, 2, 3
• – relational database management systems (RDBMS) 1
• – statistical 1, 2
• decimal scaling, normalization by 1
• decision tree induction 1, 2, 3, 4, 5
• – attribute selection measures 1
• – attribute subset selection 1, 2
• – C4.5 1, 2
• – CART 1, 2
• – gain ratio 1
• – Gini index 1
• – ID3 1, 2
• – incremental 1
• – information gain 1
• – parameters 1
• – scalability 1
• – splitting criterion 1, 2, 3
• – training datasets 1, 2, 3
• – tree pruning 1
• decision trees 1
• – branches 1, 2, 3, 4, 5
• – branches internal node 1
• – internal nodes 1
• – leaf nodes 1
• – pruning 1
• – root node 1, 2
• – rule extraction from 1 descriptive
• – distributed 1, 2, 3
• – efficiency 1, 2
• – histograms 1
• – multidimensional 1
• – incremental 1, 2
• – integration 1, 2
• – knowledge discovery 1
• – multidimensional 1
• – predictive 1, 2
• – relational databases 1
• – scalability 1
• descriptive backpropagation 1
• – efficiency 1
• Devil’s staircase 1
• Diffusion Limited Aggregation (DLA) 1, 2, 3, 4
• dimensionality reduction 1, 2
• dimensionality reduction techniques 1
• dimensions 1, 2, 3, 4, 5, 6, 7
• – association rule 1
• – concept hierarchies 1
• – pattern 1, 2, 3, 4
• – pattern 1
• – ranking 1
• – selection 1
• discrepancy detection 1, 2
• discrete attributes 1
• Discretization 1, 2, 3, 4
• – binning 1, 2
• – by clustering 1
• – correlation analysis 1
• – examples 1
• – one-mode matrix 1
• – techniques 1
• – two-mode matrix 1
• dispersion of data 1, 2
• dissimilarity 1, 2
• dissimilarity matrix 1, 2
• – as one-mode matrix 1
• – data matrix versus 1
• – distance measurements 1, 2, 3
• – n-by-n table 1
• – types of 1
• economy data analysis 1
• efficiency 1
• – data mining 1, 2, 3
• entropy value 1, 2, 3, 4
• equal-width 1, 2
• error rate/ misclassification rate 1, 2, 3, 4, 5
• Euclidean distance 1
• evaluation 1, 2
• Expanded Disability Status Scale (EDSS) 1, 2, 3, 4
• expected values 1, 2, 3
• extraction 1, 2
• extraction/transformation/loading (ETL) tools 1
• facts 1
• false negatives 1
• false positive 1