Principles of Data Mining, 3rd Edition

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.

Table of Contents

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