Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall.
Material type:
TextSeries: Morgan Kaufmann series in data management systemsPublication details: Burlington, MA : Morgan Kaufmann, c2011.Edition: 3rd edDescription: xxxiii, 629 p. : ill. ; 24 cmISBN: - 9780123748560 (pbk.)
- 0123748569 (pbk.)
- 006.3/12 22
- QA76.9.D343 W58 2011
| Cover image | Item type | Current library | Home library | Collection | Shelving location | Call number | Materials specified | Vol info | URL | Copy number | Status | Notes | Date due | Barcode | Item holds | Item hold queue priority | Course reserves | |
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Sri Lanka Institute of Development Administration | 006.3/12WIT (Browse shelf(Opens below)) | Available | 29179 |
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| 006.3RUS Artificial intelligence : | 006.76HAD Ajax for web application developers / | 006.22 WIL Making Things Smart | 006.3/12WIT Data mining : | 006.312 SIM Too Big to Ignore: | 006.686 විජේර ඇඩෝබි ඉන්ඩිසයින් : ටයිප්සෙටිං අත්පොත | 006.7/6USI Using Drupal / |
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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