Data Mining: Practical Machine Learning Tools and Techniques

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.

Please visit the book companion website at  http://www.cs.waikato.ac.nz/ml/weka/book.html

It contains

Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the bookOnline Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the bookTable of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.

Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projectsPresents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methodsIncludes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interfaceIncludes open-access online courses that introduce practical applications of the material in the book

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 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