Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall.
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TextPublication details: Burlington, MA : Morgan Kaufmann, c2011.Edition: 3rd edDescription: xxxiii, 629 p. : ill. ; 24 cmISBN: - 9780123748560
- 006.312 WIT
| Item type | Current library | Call number | Copy number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|
| Book | Mzumbe University Main Campus Library | 006.312 WIT (Browse shelf(Opens below)) | 1 | Available | 0079433 |
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| 006.312 TAN Introduction to data mining | 006.312 TAN Introduction to data mining | 006.312 TAN Introduction to data mining | 006.312 WIT Data mining : practical machine learning tools and techniques / | 006.32 KUM Neural networks: a classroom approach | 006.33 BON Expert systems : principles and practice / | 006.332 CUR Current trends in knowledge acquisition |
Includes bibliographical references (p. 587-605) and index.
Part I. Machine Learning Tools and Techniques: 1. What's iIt 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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