Description
Book SynopsisCollecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets.
Trade Review"I first taught a Ph.D. level course in business applications of data mining 10 years ago. I regularly search the web, looking for business-oriented data mining books, and this is the first one I have found that is suitable for an MS in business analytics. I plan to use it. Anyone who teaches such a class and is inclined toward R should consider this text." (Journal of the American Statistical Association, 1 January 2014)
Table of ContentsPreface ix
Acknowledgments xi
1. Introduction 1
Reference 6
2. Processing the Information and Getting to Know Your Data 7
2.1 Example 1: 2006 Birth Data 7
2.2 Example 2: Alumni Donations 17
2.3 Example 3: Orange Juice 31
References 39
3. Standard Linear Regression 40
3.1 Estimation in R 43
3.2 Example 1: Fuel Efficiency of Automobiles 43
3.3 Example 2: Toyota Used-Car Prices 47
Appendix 3.A The Effects of Model Overfitting on the Average Mean Square Error of the Regression Prediction 53
References 54
4. Local Polynomial Regression: a Nonparametric Regression Approach 55
4.1 Model Selection 56
4.2 Application to Density Estimation and the Smoothing of Histograms 58
4.3 Extension to the Multiple Regression Model 58
4.4 Examples and Software 58
References 65
5. Importance of Parsimony in Statistical Modeling 67
5.1 How Do We Guard Against False Discovery 67
References 70
6. Penalty-Based Variable Selection in Regression Models with Many Parameters (LASSO) 71
6.1 Example 1: Prostate Cancer 74
6.2 Example 2: Orange Juice 78
References 82
7. Logistic Regression 83
7.1 Building a Linear Model for Binary Response Data 83
7.2 Interpretation of the Regression Coefficients in a Logistic Regression Model 85
7.3 Statistical Inference 85
7.4 Classification of New Cases 86
7.5 Estimation in R 87
7.6 Example 1: Death Penalty Data 87
7.7 Example 2: Delayed Airplanes 92
7.8 Example 3: Loan Acceptance 100
7.9 Example 4: German Credit Data 103
References 107
8. Binary Classification, Probabilities, and Evaluating Classification Performance 108
8.1 Binary Classification 108
8.2 Using Probabilities to Make Decisions 108
8.3 Sensitivity and Specificity 109
8.4 Example: German Credit Data 109
9. Classification Using a Nearest Neighbor Analysis 115
9.1 The k-Nearest Neighbor Algorithm 116
9.2 Example 1: Forensic Glass 117
9.3 Example 2: German Credit Data 122
Reference 125
10. The Na¨ýve Bayesian Analysis: a Model for Predicting a Categorical Response from Mostly Categorical
Predictor Variables 126
10.1 Example: Delayed Airplanes 127
Reference 131
11. Multinomial Logistic Regression 132
11.1 Computer Software 134
11.2 Example 1: Forensic Glass 134
11.3 Example 2: Forensic Glass Revisited 141
Appendix 11.A Specification of a Simple Triplet Matrix 147
References 149
12. More on Classification and a Discussion on Discriminant Analysis 150
12.1 Fisher’s Linear Discriminant Function 153
12.2 Example 1: German Credit Data 154
12.3 Example 2: Fisher Iris Data 156
12.4 Example 3: Forensic Glass Data 157
12.5 Example 4: MBA Admission Data 159
Reference 160
13. Decision Trees 161
13.1 Example 1: Prostate Cancer 167
13.2 Example 2: Motorcycle Acceleration 179
13.3 Example 3: Fisher Iris Data Revisited 182
14. Further Discussion on Regression and Classification Trees, Computer Software, and Other Useful Classification Methods 185
14.1 R Packages for Tree Construction 185
14.2 Chi-Square Automatic Interaction Detection (CHAID) 186
14.3 Ensemble Methods: Bagging, Boosting, and Random Forests 188
14.4 Support Vector Machines (SVM) 192
14.5 Neural Networks 192
14.6 The R Package Rattle: A Useful Graphical User Interface for Data Mining 193
References 195
15. Clustering 196
15.1 k-Means Clustering 196
15.2 Another Way to Look at Clustering: Applying the Expectation-Maximization (EM) Algorithm to Mixtures of Normal Distributions 204
15.3 Hierarchical Clustering Procedures 212
References 219
16. Market Basket Analysis: Association Rules and Lift 220
16.1 Example 1: Online Radio 222
16.2 Example 2: Predicting Income 227
References 234
17. Dimension Reduction: Factor Models and Principal Components 235
17.1 Example 1: European Protein Consumption 238
17.2 Example 2: Monthly US Unemployment Rates 243
18. Reducing the Dimension in Regressions with Multicollinear Inputs: Principal Components Regression and Partial Least Squares 247
18.1 Three Examples 249
References 257
19. Text as Data: Text Mining and Sentiment Analysis 258
19.1 Inverse Multinomial Logistic Regression 259
19.2 Example 1: Restaurant Reviews 261
19.3 Example 2: Political Sentiment 266
Appendix 19.A Relationship Between the Gentzkow Shapiro Estimate of “Slant” and Partial Least Squares 268
References 271
20. Network Data 272
20.1 Example 1: Marriage and Power in Fifteenth Century Florence 274
20.2 Example 2: Connections in a Friendship Network 278
References 292
Appendix A: Exercises 293
Exercise 1 294
Exercise 2 294
Exercise 3 296
Exercise 4 298
Exercise 5 299
Exercise 6 300
Exercise 7 301
Appendix B: References 338
Index 341
