Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 4)
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Table of contents (22 chapters)
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Front Matter
Keywords
About this book
Knowledge Discovery today is a significant study and research area. In finding answers to many research questions in this area, the ultimate hope is that knowledge can be extracted from various forms of data around us. This book covers recent advances in unsupervised and supervised data analysis methods in Computational Intelligence for knowledge discovery. In its first part the book provides a collection of recent research on distributed clustering, self organizing maps and their recent extensions. If labeled data or data with known associations are available, we may be able to use supervised data analysis methods, such as classifying neural networks, fuzzy rule-based classifiers, and decision trees. Therefore this book presents a collection of important methods of supervised data analysis. "Classification and Clustering for Knowledge Discovery" also includes variety of applications of knowledge discovery in health, safety, commerce, mechatronics, sensor networks, and telecommunications.
Bibliographic Information
Book Title: Classification and Clustering for Knowledge Discovery
Editors: Saman Halgamuge, Lipo Wang
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/b98152
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2005
Hardcover ISBN: 978-3-540-26073-8Published: 02 September 2005
Softcover ISBN: 978-3-642-06542-2Published: 28 October 2010
eBook ISBN: 978-3-540-32404-1Published: 25 August 2005
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XII, 356
Topics: Mathematical and Computational Engineering, Artificial Intelligence, Computer Imaging, Vision, Pattern Recognition and Graphics, Computer-Aided Engineering (CAD, CAE) and Design, Applications of Mathematics, Operations Research/Decision Theory