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Data Mining 3rd Edition
V413HAV For More Quality Uploads : Kickass Torrents : https://kat.ph/user/V413HAV/ V413HAV On Facebook E-Book On Amazon Support The Developers. If You Like It, Buy It. Formats: PDF Book Description 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. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. - Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects - Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods - Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization Table of Contents 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: Advanced Data Mining Chapter 6. Implementations: Real Machine Learning Schemes Chapter 7. Data Transformation Chapter 8. Ensemble Learning Chapter 9. Moving On: Applications and Beyond Part III: The Weka Data MiningWorkbench Chapter 10. Introduction to Weka Chapter 11. The Explorer Chapter 12. The Knowledge Flow Interface Chapter 13. The Experimenter Chapter 14. The Command-Line Interface Chapter 15. Embedded Machine Learning Chapter 16. Writing New Learning Schemes Chapter 17. Tutorial Exercises for the Weka Explorer Book Details Paperback: 664 pages Publisher: Morgan Kaufmann; 3rd Edition (January 2011) Language: English ISBN-10: 0123748569 ISBN-13: 978-0123748560 Related Torrents
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