000			03334cam a2200349 a 4500
999			_c25363 _d25363
001			59174
010			_a 2013050912
020			_a9781466586741
020			_a1466586745 (hardback : acid-free paper)
040			_aDLC
082	0	0	_a005.74/1
245	0	0	_aData classification : _balgorithms and applications _cedited by Charu C. Aggarwal
260			_aBoca Raton : _bCRC Press, Taylor & Francis Group, _cc2015.
260			_c©2014
300			_axxvii, 671 p. : _bill. (some col.) ; _c26 cm.
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"--
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"--
650		0	_aFile organization (Computer science) _931883
650		0	_aCategories (Mathematics) _938431
650		0	_aAlgorithms _92438
650		7	_aBUSINESS & ECONOMICS / Statistics _92178
650		7	_aCOMPUTERS / Database Management / Data Mining _938280
650		7	_aCOMPUTERS / Machine Theory _96950
650		7	_aCOMPUTERS -- Enterprise Applications -- Business Intelligence Tools _938432
650		7	_aCOMPUTERS -- Intelligence (AI) & Semantics _938433
700	1		_aAggarwal, Charu C. _eEdited by _95835
856			_uhttps://uowd.box.com/s/emji2tyypl5bnk41qhxxpqodaug1myvj _zLocation Map
942			_cREGULAR _2ddc