Algorithms for Fuzzy Clustering

1. Front Matter

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2. Introduction

   • Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda

   Pages 1-7

3. BasicMethods for c-Means Clustering

   • Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda

   Pages 9-42

4. Variations and Generalizations - I

   • Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda

   Pages 43-66

5. Variations and Generalizations - II

   • Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda

   Pages 67-98

6. Miscellanea

   • Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda

   Pages 99-117

7. Application to Classifier Design

   • Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda

   Pages 119-155

8. Fuzzy Clustering and Probabilistic PCA Model

   • Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda

   Pages 157-169

9. Local Multivariate Analysis Based on Fuzzy Clustering

   • Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda

   Pages 171-194

10. Extended Algorithms for Local Multivariate Analysis

    • Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda

    Pages 195-233

11. Back Matter

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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.

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

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