Data Mining : Practical Machine Learning Tools and Techniques, 4/e (Paperback)

Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal

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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.

 Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. • Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
• Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
• Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.


Part I: Introduction to data mining
 Chapter 1. What’s it all about?
 Chapter 2. Input: Concepts, instances, attributes
 Chapter 3. Output: Knowledge representation
 Chapter 4. Algorithms: The basic methods
 Chapter 5. Credibility: Evaluating what’s been learned

Part II: More advanced machine learning schemes
 Chapter 6. Trees and rules
 Chapter 7. Extending instance-based and linear models
 Chapter 8. Data transformations
 Chapter 9. Probabilistic methods
 Chapter 10. Deep learning
 Chapter 11. Beyond supervised and unsupervised learning
 Chapter 12. Ensemble learning
 Chapter 13. Moving on: applications and beyond Abstract

Appendix A. Theoretical foundations
 Appendix B. The WEKA workbench