Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining' including, i.e., the rule [onions, potatoes] -> [beef] found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, he or she is also likely to buy beef.

The authors inlcude both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years.

The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new 'book release' version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on 'multi-instance learning'; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.

- Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques

- Algorithmic methods at the heart of successful data mining'including tired and true methods as well as leading edge methods

- Performance improvement techniques that work by transforming the input or output

- Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization'in an updated, interactive interface.