Fuzzy Cluster Analysis : Methods for Classification, Data Analysis and Image Recognition

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Fuzzy Cluster Analysis : Methods for Classification, Data Analysis and Image Recognition

Fuzzy Cluster Analysis : Methods for Classification, Data Analysis and Image Recognition by Etc.

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Author: Etc.
Page Count: 300 pages
Published Date: 09 Jul 1999
Publisher: John Wiley and Sons Ltd
Publication Country: Chichester, United Kingdom
Language: English
Format: PDF
ISBN: 9780471988649
File size: 22 Mb
Download Link: Fuzzy Cluster Analysis Methods for Classification, Data Analysis and Image Recognition
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Provides a timely and important introduction to fuzzy cluster analysis, its methods and areas of application, systematically describing different fuzzy clustering techniques so the user may choose methods appropriate for his problem. It provides a very thorough overview of the subject and covers classification, image recognition, data analysis and rule generation. The application examples are highly relevant and illustrative, and the use of the techniques are justified and well thought-out. 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 This is one of the most comprehensive books on fuzzy clustering and will be welcomed by computer scientists, engineers and mathematicians in industry and research who are concerned with different methods, data analysis, pattern recognition or image processing. It will also give graduate students in computer science, mathematics or statistics a valuable overview.

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