Authors:
- Gives an overall picture on how to adapt K-means to the clustering of newly emerging big data
- Establishes a theoretical framework for K-means clustering and cluster validity
- Studies the dangerous uniform effect and zero-value dilemma of K-means
- Demonstrates the novel use of K-means for rare class analysis and consensus clustering
- Based on the thesis that won the 2010 National Excellent Doctoral Dissertation Award of China
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Theses (Springer Theses)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
About this book
Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China.
Authors and Affiliations
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, School of Economics and Management, Beihang University, Beijing, China, People's Republic
Junjie Wu
Bibliographic Information
Book Title: Advances in K-means Clustering
Book Subtitle: A Data Mining Thinking
Authors: Junjie Wu
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-642-29807-3
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2012
Hardcover ISBN: 978-3-642-29806-6Published: 10 July 2012
Softcover ISBN: 978-3-642-44757-0Published: 09 August 2014
eBook ISBN: 978-3-642-29807-3Published: 09 July 2012
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XVI, 180
Topics: Data Mining and Knowledge Discovery, Statistics for Business, Management, Economics, Finance, Insurance, IT in Business, Database Management