000			02684nam a2200349 i 4500
001			CR9781108635592
003			UkCbUP
005			20240906165202.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			180606s2019||||enk o ||1 0|eng|d
020			_a9781108635592 (ebook)
020			_z9781108727747 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA76.9.D343 _bB435 2019
082	0	0	_a006.3/12 _223
100	1		_aBhatia, Parteek, _eauthor.
245	1	0	_aData mining and data warehousing : _bprinciples and practical techniques / _cParteek Bhatia.
264		1	_aCambridge : _bCambridge University Press, _c2019.
300			_a1 online resource (xxxiv, 468 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 02 May 2019).
505	0		_aBeginning with machine learning -- Introduction to data mining -- Beginning with Weka and R language -- Data preprocessing -- Classification -- Implementing classification in Weka and R -- Cluster analysis -- Implementing clustering with Weka and R -- Association mining -- Implementing association mining with Weka and R -- Web mining and search engines -- Data warehouse -- Data warehouse schema -- Online analytical processing -- Big data and NoSQL.
520			_aWritten in lucid language, this valuable textbook 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 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. 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. Pedagogical features including unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding.
650		0	_aData mining _vTextbooks.
650		0	_aData warehousing _vTextbooks.
776	0	8	_iPrint version: _z9781108727747
856	4	0	_uhttps://doi.org/10.1017/9781108635592
942			_2ddc _cEB
999			_c9696 _d9696