Algorithms for Fuzzy Clustering

Methods in c-Means Clustering with Applications

  • Authors
  • Sadaaki Miyamoto
  • Hidetomo Ichihashi
  • Katsuhiro Honda

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 229)

Table of contents

  1. Front Matter
  2. Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda
    Pages 1-7
  3. Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda
    Pages 9-42
  4. Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda
    Pages 43-66
  5. Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda
    Pages 67-98
  6. Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda
    Pages 99-117
  7. Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda
    Pages 119-155
  8. Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda
    Pages 157-169
  9. Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda
    Pages 171-194
  10. Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda
    Pages 195-233
  11. Back Matter

About this book

Introduction

The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reason why we concentrate on fuzzy c-means is that most methodology and application studies in fuzzy clustering use fuzzy c-means, and hence fuzzy c-means should be considered to be a major technique of clustering in general, regardless whether one is interested in fuzzy methods or not. Unlike most studies in fuzzy c-means, what we emphasize in this book is a family of algorithms using entropy or entropy-regularized methods which are less known, but we consider the entropy-based method to be another useful method of fuzzy c-means. Throughout this book one of our intentions is to uncover theoretical and methodological differences between the Dunn and Bezdek traditional method and the entropy-based method. We do note claim that the entropy-based method is better than the traditional method, but we believe that the methods of fuzzy c-means become complete by  adding the entropy-based method to the method by Dunn and Bezdek, since we can observe natures of the both methods more deeply by contrasting these two.

Keywords

Fuzziness Fuzzy Fuzzy Clustering algorithm algorithms c-Means Clusterin calculus model

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-78737-2
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-78736-5
  • Online ISBN 978-3-540-78737-2
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • About this book