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Springer Theses

Advances in K-means Clustering

A Data Mining Thinking

Authors: Wu, Junjie

  • 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
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eBook $99.00
price for USA
  • ISBN 978-3-642-29807-3
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Hardcover $129.00
price for USA
  • ISBN 978-3-642-29806-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $129.00
price for USA
  • ISBN 978-3-642-44757-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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.

Table of contents (7 chapters)

  • Cluster Analysis and K-means Clustering: An Introduction

    Wu, Junjie

    Pages 1-16

  • The Uniform Effect of K-means Clustering

    Wu, Junjie

    Pages 17-35

  • Generalizing Distance Functions for Fuzzy c-Means Clustering

    Wu, Junjie

    Pages 37-67

  • Information-Theoretic K-means for Text Clustering

    Wu, Junjie

    Pages 69-98

  • Selecting External Validation Measures for K-means Clustering

    Wu, Junjie

    Pages 99-123

Buy this book

eBook $99.00
price for USA
  • ISBN 978-3-642-29807-3
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Hardcover $129.00
price for USA
  • ISBN 978-3-642-29806-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $129.00
price for USA
  • ISBN 978-3-642-44757-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Advances in K-means Clustering
Book Subtitle
A Data Mining Thinking
Authors
Series Title
Springer Theses
Copyright
2012
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-29807-3
DOI
10.1007/978-3-642-29807-3
Hardcover ISBN
978-3-642-29806-6
Softcover ISBN
978-3-642-44757-0
Series ISSN
2190-5053
Edition Number
1
Number of Pages
XVI, 180
Topics