Preface.

Acknowledgments.

1. Introduction.

2. Overview of the Data Mining Process.

3. Data Exploration and Dimension Reduction.

4. Evaluating Classification and Predictive Performance.

5. Multiple Linear Regression.

6. Three Simple Classification Methods.

7. Classification and Regression trees.

8. Logistic Regression.

9. Neural Nets.

10. Discriminant Analysis.

11. Association Rules.

12. Cluster Analysis.

13. Cases.

References.

Index.