Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall.
Material type:
TextPublication details: Burlington, MA : Morgan Kaufmann, c2011.Edition: 3rd edDescription: xxxiii, 629 p. : ill. ; 24 cmISBN: - 9780123748560
- 006.3/12 22
- QA76.9.D343 W58 2011
| Item type | Current library | Collection | Call number | Vol info | Status | Date due | Barcode |
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Main Long
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Martin Oduor-Otieno Library This item is located on the library Second Floor | Non-fiction | QA76.9.D343 W58 2011 (Browse shelf(Opens below)) | 27656/15 | Available | MOOL15020335 |
Includes bibliographical references 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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