000			02713cam a22003015a 4500
008			090409a1999 caua b 001 0 eng
010			_a99046067
020			_a9781558605527
020			_a1558605525
035			_a(Sirsi) u1356
040			_aEG-CaNU _cEG-CaNU _dEG-CaNU
042			_ancode
082	0	0	_a006.3 _2 21
100	1		_aWitten, I. H. _q (Ian H.) _9190
245	1	0	_aData mining : _b practical machine learning tools and techniques with Java implementations / _c Ian H. Witten, Eibe Frank.
260			_aSan Francisco, Calif. : _b Morgan Kaufmann, _c 2000.
300			_axxv, 371 p. ; _c 24 cm.
504			_aIncludes bibliographical references(p. 339-349) and index.
520			_aThe third edition of Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, as well as a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.
505	0		_aCh 1 What’s It All About? -- Ch 2 Input: Concepts, Instances, Attributes -- Ch 3 Output: Knowledge Representation -- Ch 4 Algorithms: The Basic Methods -- Ch 5 Credibility: Evaluating What’s Been Learned -- Ch 6 Implementations: Real Machine Learning Schemes -- Ch 7 Data Transformation -- Ch 8 Ensemble Learning -- Ch 9 Moving On: Applications and Beyond -- Ch 10 Introduction to Weka -- Ch 11 The Explorer -- Ch 12 The Knowledge Flow Interface -- Ch 13 The Experimenter -- Ch 14 The Command-Line Interface -- Ch 15 Embedded Machine Learning -- Ch 16 Writing New Learning Schemes -- Ch 17 Tutorial Exercises for the Weka Explorer.
630			_aData Mining _91367
650		0	_aData mining _91368
650		0	_aJava (Computer program language) _9156
700	1		_aFrank, Eibe _91369
596			_a1
999			_c396 _d396