000			01336cam a22002054a 4500
020			_a9780123748560 _cTZS 166250/=
040			_aMUL _beng _eAACR
082	0	0	_a006.312 WIT
100	1		_aWitten, I. H.
245	1	0	_aData mining : _bpractical machine learning tools and techniques / _cIan H. Witten, Eibe Frank, Mark A. Hall.
250			_a3rd ed.
260			_aBurlington, MA : _bMorgan Kaufmann, _cc2011.
300			_axxxiii, 629 p. : _bill. ; _c24 cm.
504			_aIncludes bibliographical references (p. 587-605) and index.
505	0		_aPart 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.
650		0	_aData mining.
700	1		_aFrank, Eibe.
700	1		_aHall, Mark A.
942			_cBK
999			_c7046 _d7046