Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall.
Material type:
TextSeries: Publication details: Burlington, MA : Morgan Kaufmann, c2011.Edition: 3rd edDescription: xxxiii, 629 p. : illISBN: - 9780123748560 (pbk.)
- 0123748569 (pbk.)
- 006.312 22 WIT
| Item type | Current library | Home library | Collection | Call number | Status | Date due | Barcode | |
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Dept. of Futures Studies Processing Center | Dept. of Futures Studies | Knowledge Management | 006.312 WIT (Browse shelf(Opens below)) | Available | DFSKM3 |
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| 006.31 TUR Computable Models/ | 006.312 GUP Introduction to data mining with case studies / | 006.312 TAN Introduction to data mining / | 006.312 WIT Data mining : practical machine learning tools and techniques / | 006.312 WIT;1 Data mining : practical machine learning tools and techniques / | 006.32 PRI Artificial Neural Networks: An Introduction/ | 006.32 SIV Introduction to artificial neural networks / |
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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