Fuzzy Cluster Analysis

Fuzzy Cluster Analysis presents advanced and powerful fuzzy clustering techniques. This thorough and self-contained introduction to fuzzy clustering methods and applications covers classification, image recognition, data analysis and rule generation. Combining theoretical and practical perspectives, each method is analysed in detail and fully illustrated with examples. Features include:
* Sections on inducing fuzzy if-then rules by fuzzy clustering and non-alternating optimization fuzzy clustering algorithms
* Discussion of solid fuzzy clustering techniques like the fuzzy c-means, the Gustafson-Kessel and the Gath-and-Geva algorithm for classification problems
* Focus on linear and shell clustering techniques used for detecting contours in image analysis
* Accompanying software and data sets pertaining to the examples presented, enabling the reader to learn through experimentation
* Examination of the difficulties involved in evaluating the results of fuzzy cluster analysis and of determining the number of clusters with analysis of global and local validity measures
* Description of different fuzzy clustering techniques allowing the user to select the method most appropriate to a particular problem
Computer scientists, engineers and mathematicians in industry and research who are concerned with fuzzy clustering methods, data analysis, pattern recognition or image processing will find this a timely and accessible resource. Graduate students in computer science, mathematics or statistics will value this comprehensive overview of the applications of fuzzy methods. Download accompanying program and data sets from our website

Introduction.

Basic Concepts.

Classical Fuzzy Clustering Algorithms.

Linear and Ellipsoidal Prototypes

Shell Prototypes.

Polygonal Object Boundaries.

Cluster Estimation Models.

Cluster Validity.

Rule Generation with Clustering.

Appendix.

Bibliography.