Data mining : practical machine learning tools and techniques / by Ian H. Witten, Eibe Frank, Mark A. Hall.
Material type: TextSeries: Publication details: New Delhi : Elsevier, 2013Edition: 3rd edDescription: v, 629 p. : ill. ; 24 cmISBN: 9780123748560 (pbk.); 9789380501864 (pbk.)Subject(s): Data miningDDC classification: 006.3Item type | Current library | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|
Books | Namal Library Computer Science | 006.3 WIT-D 2013 4451 (Browse shelf (Opens below)) | Available | 0004451 |
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.
There are no comments on this title.