Algorithms for fuzzy clustering. Methods in \(c\)-means clustering with applications. (English) Zbl 1147.68073

Studies in Fuzziness and Soft Computing 229. Berlin: Springer (ISBN 978-3-540-78736-5/hbk). xi, 247 p. (2008).
Publisher’s description: 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.


68T10 Pattern recognition, speech recognition
68P05 Data structures
68-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to computer science
68T05 Learning and adaptive systems in artificial intelligence


fuzzy c-means


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