Includes bibliographical references and index.

Part I : Introduction to data mining.
1. What's it all about? --
2. Input : concepts, instances, and attributes --
3. Output : knowledge representation --
4. Algorithms : the basic methods --
5. Credibility : evaluating what's been learned --
Part II : More advanced machine learning schemes.
6. Trees and rules --
7. Extending instance-based and linear models --
8. Data transformations --
9. Probabilistic methods --
10. Deep learning --
11. Beyond supervised and unsupervised learning --
12. Ensemble learning --
13. Moving on : applications and beyond.

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.