Cover -- Title -- Copyright -- Contents -- Acknowledgments -- PART 1. CONCEPTS -- 1. What Is Data Mining? -- The Goals of This Book -- Software and Hardware for Data Mining -- Basic Terminology -- 2. Contrasts with the Conventional Statistical Approach -- Predictive Power in Conventional Statistical Modeling -- Hypothesis Testing in the Conventional Approach -- Heteroscedasticity as a Threat to Validity in Conventional Modeling -- The Challenge of Complex and Nonrandom Samples -- Bootstrapping and Permutation Tests -- Nonlinearity in Conventional Predictive Models -- Statistical Interactions in Conventional Models -- Conclusion -- 3. Some General Strategies Used in Data Mining -- Cross-Validation -- Overfitting -- Boosting -- Calibrating -- Measuring Fit: The Confusion Matrix and ROC Curves -- Identifying Statistical Interactions and Effect Heterogeneity in Data Mining -- Bagging and Random Forests -- The Limits of Prediction -- Big Data Is Never Big Enough -- 4. Important Stages in a Data Mining Project -- When to Sample Big Data -- Building a Rich Array of Features -- Feature Selection -- Feature Extraction -- Constructing a Model -- PART 2. WORKED EXAMPLES -- 5. Preparing Training and Test Datasets -- The Logic of Cross-Validation -- Cross-Validation Methods: An Overview -- 6. Variable Selection Tools -- Stepwise Regression -- The LASSO -- VIF Regression -- 7. Creating New Variables Using Binning and Trees -- Discretizing a Continuous Predictor -- Continuous Outcomes and Continuous Predictors -- Binning Categorical Predictors -- Using Partition Trees to Study Interactions -- 8. Extracting Variables -- Principal Component Analysis -- Independent Component Analysis -- 9. Classifiers -- K-Nearest Neighbors -- Naive Bayes -- Support Vector Machines -- Optimizing Prediction across Multiple Classifiers -- 10. Classification Trees -- Partition Trees.

Boosted Trees and Random Forests -- 11. Neural Networks -- 12. Clustering -- Hierarchical Clustering -- K-Means Clustering -- Normal Mixtures -- Self-Organized Maps -- 13. Latent Class Analysis and Mixture Models -- Latent Class Analysis -- Latent Class Regression -- Mixture Models -- 14. Association Rules -- Conclusion -- Bibliography -- Notes -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- R -- S -- T -- U -- V -- W -- X -- Y -- Z.