Algorithms for Fuzzy Clustering : Methods in c-Means Clustering with Applications (Studies in Fuzziness and Soft Computing) 〈Vol. 229〉

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Algorithms for Fuzzy Clustering : Methods in c-Means Clustering with Applications (Studies in Fuzziness and Soft Computing) 〈Vol. 229〉

Miyamoto, S./ Ichihashi, H.

ウェブストア価格 ¥34,943（本体¥31,767）
Springer（2008/04発売）
外貨定価 US$ 169.99
ポイント 317pt

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製本 Hardcover:ハードカバー版／ページ数 255 p.
言語 ENG
商品コード 9783540787365
DDC分類 511

Full Description

Recently many researchers are working on cluster analysis as a main tool for exploratory data analysis and data mining. A notable feature is that specialists in di?erent ?elds of sciences are considering the tool of data clustering to be useful. A major reason is that clustering algorithms and software are ?exible in thesensethatdi?erentmathematicalframeworksareemployedinthealgorithms and a user can select a suitable method according to his application. Moreover clusteringalgorithmshavedi?erentoutputsrangingfromtheolddendrogramsof agglomerativeclustering to more recent self-organizingmaps. Thus, a researcher or user can choose an appropriate output suited to his purpose,which is another ?exibility of the methods of clustering. An old and still most popular method is the K-means which use K cluster centers. A group of data is gathered around a cluster center and thus forms a cluster. The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reasonwhy we concentrate on fuzzy c-means is that most methodology and application studies infuzzy clusteringusefuzzy c-means,andfuzzy c-meansshouldbe consideredto beamajortechniqueofclusteringingeneral,regardlesswhetheroneisinterested in fuzzy methods or not. Moreover recent advances in clustering techniques are rapid and we requirea new textbook that includes recent algorithms.We should also note that several books have recently been published but the contents do not include some methods studied herein.

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.