000			02061cam a2200277 i 4500
001			20922191
005			20250320020011.0
008			190403s2019 enka b 001 0 eng c
010			_a 2019013358
020			_a9781108727747
040			_aLBSOR/DLC _beng _erda _cLBSOR _dDLC
042			_apcc
050	0	0	_aQA76.9.D343 _bB43 2019
082	0	0	_a006.3/12 _223
100	1		_aBhatia, Parteek, _eauthor. _927301
245	1	0	_aData mining and data warehousing : _bprinciples and practical techniques / _cParteek Bhatia.
260			_aNew York: _bCambridge University Press, _cC2019
300			_axxxiv, 477 pages : _billustrations (some color) ; _c24 cm
504			_aIncludes bibliographical references and index.
520			_a"This textbook is written to cater to the needs of undergraduate students of computer science, engineering, and information technology for a course on data mining and data warehousing. It brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models, and NoSQL are discussed in detail. Unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding"--
650		0	_aData mining _vTextbooks. _927302
650		0	_aData warehousing _vTextbooks. _927303
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK _hQA76.9.D343 _kQA76.9.D343 _mB43 2019 _03
999			_c9395 _d9395