This book explains the principal techniques of data mining, for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed examples, with a focus on algorithms rather than mathematical formalism.
Chapter 1. Introduction to Data Mining
Chapter 2. Data for Data Mining
Chapter 3. Introduction to Classification: Naïve Bayes and Nearest Neighbour
Chapter 4. Using Decision Trees for Classification
Chapter 5. Decision Tree Induction: Using Entropy for Attribute Selection
Chapter 6. Decision Tree Induction: Using Frequency Tables for Attribute Selection
Chapter 7. Estimating the Predictive Accuracy of a Classifier
Chapter 8. Continuous Attributes
Chapter 9. Avoiding Overfitting of Decision Trees
Chapter 10. More About Entropy
Chapter 11. Inducing Modular Rules for Classification
Chapter 12. Measuring the Performance of a Classifier
Chapter 13. Dealing with Large Volumes of Data
Chapter 14. Ensemble Classification
Chapter 15. Comparing Classifiers
Chapter 16. Association Rule Mining I
Chapter 17. Association Rule Mining II
Chapter 18. Association Rule Mining III: Frequent Pattern Trees
Chapter 19. Clustering
Chapter 20. Text Mining
Chapter 21. Classifying Streaming Data
Chapter 22. Classifying Streaming Data II: Time-Dependent Data
Appendix A. Essential Mathematics
Appendix B. Datasets
Appendix C. Sources of Further Information
Appendix D. Glossary and Notation
Appendix E. Solutions to Self-assessment Exercises
