03334cam a2200349 a 4500
25363
25363
59174
2013050912
9781466586741
1466586745 (hardback : acid-free paper)
DLC
005.74/1
Data classification :
algorithms and applications
edited by Charu C. Aggarwal
Boca Raton :
CRC Press, Taylor & Francis Group,
c2015.
©2014
xxvii, 671 p. :
ill. (some col.) ;
26 cm.
Chapman & Hall/CRC data mining and knowledge discovery series
"A Chapman & Hall book."
Includes bibliographical references and index.
"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"--
"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"--
File organization (Computer science)
31883
Categories (Mathematics)
38431
Algorithms
2438
BUSINESS & ECONOMICS / Statistics
2178
COMPUTERS / Database Management / Data Mining
38280
COMPUTERS / Machine Theory
6950
COMPUTERS -- Enterprise Applications -- Business Intelligence Tools
38432
COMPUTERS -- Intelligence (AI) & Semantics
38433
Aggarwal, Charu C.
Edited by
5835
https://uowd.box.com/s/emji2tyypl5bnk41qhxxpqodaug1myvj
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