000			01979cam a22003015i 4500
003			EG-CaNGU
005			20240131134626.0
008			231218t2017 ne a frb 001 0 eng d
020			_a9780128042915
040			_aDLC _beng _erda _cDLC _dEG-CaNGU
082	0	4	_a006.312 _bWID _223
100	1		_aWitten, I. H., _q(Ian H.), _eauthor. _96030
245	1	0	_aData mining : _bpractical machine learning tools and techniques /
250			_aFourth Edition.
264		1	_aAmsterdam, Netherlands : _bMorgan Kaufmann Publishers, Elsevier, _c[2017]
264		4	_c©2017
300			_axxxii, 621 pages : _billustrations ; _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0	0	_aPart I: Introduction to data mining -- Chapter 1. What’s it all about? -- Chapter 2. Input: Concepts, instances, attributes -- Chapter 3. Output: Knowledge representation -- Chapter 4. Algorithms: The basic methods -- Chapter 5. Credibility: Evaluating what’s been learned -- Part II. More advanced machine learning schemes -- Chapter 6. Trees and rules -- Chapter 7. Extending instance-based and linear models -- Chapter 8. Data transformations -- Chapter 9. Probabilistic methods -- Chapter 10. Deep learning -- Chapter 11. Beyond supervised and unsupervised learning -- Chapter 12. Ensemble learning -- Chapter 13. Moving on: applications and beyond -- References -- Index.
650		7	_aData mining. _2NGU-sh _93604
700	1		_aFrank, Eibe, _eauthor. _96031
700	1		_aHall, Mark A., _q(Mark Andrew), _eauthor. _96032
700	1		_aPal, Christopher J., _eauthor. _96033
999			_c1985 _d1985