Data mining : practical machine learning tools and techniques / / Ian H. Witten, Eibe Frank, Mark A. Hall.
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| Year of Publication: | 2011 |
| Edition: | 3rd ed. |
| Language: | English |
| Online Access: | |
| Physical Description: | xxxiii, 629 p. :; ill. |
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Table of 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.