Data Classification
Algorithms and Applications
1st Edition
Published on 25. July 2014
707 pages
978-1-4665-8675-8 (ISBN)
Description
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
More details
Series
Edition
1. Auflage
Language
English
Place of publication
Philadelphia, PA
United States
Publishing group
Taylor & Francis Inc
Target group
Professional and scholarly
Illustrations
84 s/w Abbildungen, 34 s/w Tabellen
84 b/w images and 34 tables
File size
11,85 MB
ISBN-13
978-1-4665-8675-8 (9781466586758)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
09/2020
1st Edition
Chapman & Hall/CRC
€59.46
Shipment within 15-20 days
Book
07/2014
1st Edition
CRC Press
€237.70
Shipment within 15-20 days
Person
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.
Content
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.
