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Data Analysis and Data Mining

Preface vii

Preface to the English Edition ix
1. Introduction 1
1.1. New problems and new opportunities 1
1.2. All models are wrong 9
1.3. A matter of style 12
2. A–B–C 15
2.1. Old friends: Linear models 15
2.2. Computational aspects 30
2.3. Likelihood 33
2.4. Logistic regression and GLM 40
Exercises 44
3. Optimism, Conflicts, and Trade-offs 45
3.1. Matching the conceptual frame and real life 45
3.2. A simple prototype problem 46
3.3. If we knew f (x). . . 47
3.4. But as we do not know f (x). . . 51
3.5. Methods for model selection 52
3.6. Reduction of dimensions and selection of most
appropriate model 58
Exercises 66
4. Prediction of Quantitative Variables 68
4.1. Nonparametric estimation: Why? 68
4.2. Local regression 69
4.3. The curse of dimensionality 78
4.4. Splines 79
4.5. Additive models and GAM 89
4.6. Projection pursuit 93
4.7. Inferential aspects 94
4.8. Regression trees 98
4.9. Neural networks 106
4.10. Case studies 111
Exercises 132
vi CONTENTS
5. Methods of Classification 134
5.1. Prediction of categorical variables 134
5.2. An introduction based on a marketing problem 135
5.3. Extension to several categories 142
5.4. Classification via linear regression 149
5.5. Discriminant analysis 154
5.6. Some nonparametric methods 159
5.7. Classification trees 164
5.8. Some other topics 168
5.9. Combination of classifiers 176
5.10. Case studies 183
Exercises 210
6. Methods of Internal Analysis 212
6.1. Cluster analysis 212
6.2. Associations among variables 222
6.3. Case study: Web usage mining 232
Appendix A Complements of Mathematics and Statistics 240
A.1. Concepts on linear algebra 240
A.2. Concepts of probability theory 241
A.3. Concepts of linear models 246
Appendix B Data Sets 254
B.1. Simulated data 254
B.2. Car data 254
B.3. Brazilian bank data 255
B.4. Data for telephone company customers 256
B.5. Insurance data 257
B.6. Choice of fruit juice data 258
B.7. Customer satisfaction 259
B.8. Web usage data 261
AppendixC Symbols and Acronyms 263
References 265
Author Index 269
Subject Index 271

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