000 03334cam a2200349 a 4500
999 _c25363
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,
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)
650 0 _aCategories (Mathematics)
650 0 _aAlgorithms
650 7 _aBUSINESS & ECONOMICS / Statistics
650 7 _aCOMPUTERS / Database Management / Data Mining
650 7 _aCOMPUTERS / Machine Theory
650 7 _aCOMPUTERS -- Enterprise Applications -- Business Intelligence Tools
650 7 _aCOMPUTERS -- Intelligence (AI) & Semantics
700 1 _aAggarwal, Charu C.
_eEdited by
856 _uhttps://uowd.box.com/s/emji2tyypl5bnk41qhxxpqodaug1myvj
_zLocation Map