Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition (Wiley IBM PC Series) [Hardcover]

Frank Höppner (Author), Frank Klawonn (Author), Rudolf Kruse (Author), Thomas Runkler (Author)

Book Description

Publication Date: June 29, 1999 | ISBN-10: 0471988642 | ISBN-13: 978-0471988649 | Edition: 1

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.

Editorial Reviews

Language Notes

Text: English (translation)
Original Language: German

From the Back Cover

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

Product Details

• Hardcover: 300 pages
• Publisher: Wiley; 1 edition (June 29, 1999)
• Language: English
• ISBN-10: 0471988642
• ISBN-13: 978-0471988649
• Product Dimensions: 9.3 x 6.3 x 0.9 inches
• Shipping Weight: 1.2 pounds (View shipping rates and policies)
• Average Customer Review: 2.0 out of 5 stars  See all reviews (3 customer reviews)
• Amazon Best Sellers Rank: #3,054,555 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

4 of 4 people found the following review helpful:

4.0 out of 5 stars Excellent advanced book in fuzzy cluster analysis, May 11, 2004

By A Customer

This review is from: Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition (Wiley IBM PC Series) (Hardcover)

Previous reviews are correct, this is not a book for beginners. This is an advanced text for specialists in the area of fuzzy clustering, and as such it is an excellent work. Standard fuzzy c-means, Gustafson-Kessel c-means and Gath-Geva c-means are drawn together in a single modeling framework for near-arbitrary cluster shapes. The cluster validity problem is examined in depth, and experimental results in image analysis are used to illustrate the theoretical material. An excellent monograph.

2 of 5 people found the following review helpful:

1.0 out of 5 stars Worst book I have ever read, March 23, 2004

By 

"smallstore" (Chicago, IL United States) - See all my reviews

This review is from: Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition (Wiley IBM PC Series) (Hardcover)

This book is ridiculous. It has too many unnecessary and, even worse, unclear definitions. It never gives us the meanings of the strange symbols in those absurd definitions. If you want to understand fuzzy cluster analysis in a relatively short time and do not want to suffer, please do not buy this book.

1 of 4 people found the following review helpful:

1.0 out of 5 stars Too many unnecessary definitions, March 23, 2004

By A Customer

This review is from: Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition (Wiley IBM PC Series) (Hardcover)

This is not a good book for people who want to understand the basic idea of fuzzy cluster analysis in a short time.