Data Classification

Algorithms and Applications

AvCharu C. Aggarwal

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PDF, Engelska, 2014

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Beskrivning

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 underlyi

Produktinformation

Utgivningsdatum:2014-07-25
Språk:Engelska
Filformat:PDF
Kopieringsskydd:LCP
ISBN:9781466586758
Förlag:Taylor & Francis Inc
Serie:Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Utforska kategorier

Databaser inom Data och IT
Systemvetenskap och AI inom Data och IT

Mer om författaren

Charu C. Aggarwal is a research scientist at the IBM T.J. Watson Research Center. A fellow of the IEEE and the ACM, he is the author/editor of ten books, an associate editor of several journals, and the vice-president of the SIAM Activity Group on Data Mining. Dr. Aggarwal has published over 200 papers, has applied for or been granted over 80 patents, and has received numerous honors, including the IBM Outstanding Technical Achievement Award and EDBT 2014 Test of Time Award. His research interests include performance analysis, databases, and data mining. He earned a Ph.D. from the Massachusetts Institute of Technology.

Innehållsförteckning

An Introduction to Data Classification. Feature Selection for Classification: A Review. Probabilistic Models for Classification. Decision Trees: Theory and Algorithms. Rule-Based Classification. Instance-Based Learning: A Survey. Support Vector Machines. Neural Networks: A Review. A Survey of Stream Classification Algorithms. Big Data Classification. Text Classification. Multimedia Classification. Time Series Data Classification. Discrete Sequence Classification. Collective Classification of Network Data. Uncertain Data Classification. Rare Class Learning. Distance Metric Learning for Data Classification. Ensemble Learning. Semi-Supervised Learning. Transfer Learning. Active Learning: A Survey. Visual Classification. Evaluation of Classification Methods. Educational and Software Resources for Data Classification. Index.