โ Scribed by Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda (auth.)
No coin nor oath required. For personal study only.
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.
Front Matter....Pages -
Introduction....Pages 1-7
BasicMethods for c -Means Clustering....Pages 9-42
Variations and Generalizations - I....Pages 43-66
Variations and Generalizations - II....Pages 67-98
Miscellanea....Pages 99-117
Application to Classifier Design....Pages 119-155
Fuzzy Clustering and Probabilistic PCA Model....Pages 157-169
Local Multivariate Analysis Based on Fuzzy Clustering....Pages 171-194
Extended Algorithms for Local Multivariate Analysis....Pages 195-233
Back Matter....Pages -
Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics)
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<p><P>The main subject of this book is the fuzzy <EM>c</EM>-means proposed by Dunn and Bezdek and their variations including recent studies. A main reason why we concentrate on fuzzy <EM>c</EM>-means is that most methodology and application studies in fuzzy clustering use fuzzy <EM>c</EM>-means, and
Department of Computing Imperial College of Science, Technology and Medicine<br/>University of London, London SW7 2AZ.<br/>A dissertation submitted in partial fulfilment of the requirements<br/>for the degree of Doctor of Philosophy of the University of London.<div class="bb-sep"></div>Abstract<br/>
<b>A comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering . <p> Fuzzy clustering is now a mature and vibrant area of research with highly innovative advanced applications. Encapsulating this through presenting a careful selection of research contributi
<b>A comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering . <p> Fuzzy clustering is now a mature and vibrant area of research with highly innovative advanced applications. Encapsulating this through presenting a careful selection of research contributi
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