Data mining [electronic resource] : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall.

By: Contributor(s): Material type: TextTextPublication details: Amsterdam : Elsevier/Morgan Kaufmann, 2011.Edition: 3rd edDescription: xxxiii, 629 p. : illSubject(s): Genre/Form: DDC classification:
  • 006.3/12 22
LOC classification:
  • QA76.9.D343 W58 2011eb
Online resources:
Contents:
Part I. Machine learning tools and techniques: 1. What's it all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what's been learned -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer.
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Includes bibliographical references and index.

Part I. Machine learning tools and techniques: 1. What's it all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what's been learned -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer.

Electronic reproduction. Palo Alto, Calif. : ebrary, 2012. Available via World Wide Web. Access may be limited to ebrary affiliated libraries.

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