000			03676cam a2200421 i 4500
001			18053571
005			20191128155908.0
008			140304t20142014flua b 001 0 eng
010			_a 2013050912
020			_a9781466586741 (hardback : acidfree paper)
040			_aDLC _beng _erda _dDLC
042			_apcc
050	0	0	_aQA76.9.F5 _b.D38 2014
082	0	0	_a005.74/1 _223
084			_aBUS061000 _aCOM021030 _aCOM037000 _2bisacsh
245	0	0	_aData classification : _balgorithms and applications / _cedited by Charu C. Aggarwal, IBM T. J. Watson Research Center, Yorktown Heights, New York, USA .
264		1	_aBoca Raton : _bCRC Press, Taylor & Francis Group, _c[2014]
264		4	_c©2014
300			_axxvii, 671 pages : _billustrations (some color) ; _c26 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
490	0		_aChapman & Hall/CRC data mining and knowledge discovery series
500			_a"A Chapman & Hall book."
504			_aIncludes bibliographical references and index.
520			_a"Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data.This comprehensive book focuses on three primary aspects of data classification:MethodsThe book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks. DomainsThe book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm. VariationsThe book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers"-- _cProvided by publisher.
520			_a"This book homes in on three primary aspects of data classification: the core methods for data classification including probabilistic classification, decision trees, rule-based methods, and SVM methods; different problem domains and scenarios such as multimedia data, text data, biological data, categorical data, network data, data streams and uncertain data: and different variations of the classification problem such as ensemble methods, visual methods, transfer learning, semi-supervised methods and active learning. These advanced methods can be used to enhance the quality of the underlying classification results"-- _cProvided by publisher.
650		0	_aFile organization (Computer science)
650		0	_aCategories (Mathematics)
650		0	_aAlgorithms.
650		7	_aBUSINESS & ECONOMICS / Statistics. _2bisacsh
650		7	_aCOMPUTERS / Database Management / Data Mining. _2bisacsh
650		7	_aCOMPUTERS / Machine Theory. _2bisacsh
700	1		_aAggarwal, Charu C.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2udc _cBK
999			_c28091 _d28091