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Advances in K-means Clustering

A Data Mining Thinking

  • Junjie Wu

Part of the Springer Theses book series (Springer Theses)

Table of contents

About this book

Introduction

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.

Keywords

Cluster Analysis Cluster Validity Consensus Clustering Information-Theoretic Clustering K-means Point-to-Centroid Distance Rare Class Analysis Uniform Effect

Authors and affiliations

  • Junjie Wu
    • 1
  1. 1., School of Economics and ManagementBeihang UniversityBeijingChina, People's Republic

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-29807-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-29806-6
  • Online ISBN 978-3-642-29807-3
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
  • Buy this book on publisher's site
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