Algorithms for Fuzzy Clustering : Methods in c-Means Clustering with Applications /
By: Miyamoto, Sadaaki [author.].
Contributor(s): Ichihashi, Hidetomo [author.] | Honda, Katsuhiro [author.] | SpringerLink (Online service).
Material type:
BookSeries: Studies in Fuzziness and Soft Computing: 229Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.Description: XI, 247 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540787372.Subject(s): Computer science | Computer programming | Artificial intelligence | Applied mathematics | Engineering mathematics | Computer Science | Programming Techniques | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics)DDC classification: 005.11 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBK890 |
BasicMethods for c-Means Clustering -- Variations and Generalizations - I -- Variations and Generalizations - II -- Miscellanea -- Application to Classifier Design -- Fuzzy Clustering and Probabilistic PCA Model -- Local Multivariate Analysis Based on Fuzzy Clustering -- Extended Algorithms for Local Multivariate Analysis.
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.
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