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.
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
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- Morgan Kaufmann
Continues to lead introductions to data mining I’ve read and reviewed the 1st, 2nd and now the 4th edition. The authors are genuine experts, at the front of their fields, and by adding new contributors have been able to both update existing topics as well as add authoritative treatments of new ones. I recommend this text to anyone seeking a serious introduction to data mining. The emphasis is practical rather than theoretical, but there are pointers to the theoretical literature for those wanting them. The practical emphasis serves those…
Poorly Written, Insufficient Structure, Flighty Author, Useless SW Tool I am using this text in a University (American) Data Mining Certification Program. This book is horrible for learning — truly dreadful attempt by an obviously disinterested professor. It does not help that a worthless SW program is used in the course, Weka, which is hardly recognized within the industry. And for good reason: Weka (termed for some New Zealand bird??) is clunky: the user-interface is poorly designed, the program accepts minimal hyperparameters, and the graphic output is so…
Great text for the subject matter but i think this edition needs some editing to fix reference errors This is a great textbook for the subject, but this edition has some significant typos in it. The book i received has significant errors in reference to chapters in the book. For example, the opening to part two of the book references the later chapters all incorrectly. The book seems to be legit as far as being genuine so i don’t think i got a knock-off version.