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

ISBN-13: 9780471988649 / Angielski / Twarda / 1999 / 304 str.

Frank Hopner; Frank Hoppner; Frank Klawonn

Opis

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.

Spis treści

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.

O autorze

Prof. Dr. Frank Klawonn ist an der FH Braunschweig (Bereich Data Mining, Fuzzy-Systeme, neuronale Netze und evolutionäre Algorithmen) tätig. Prof. Dr. Rudolf Kruse ist Leiter des Lehrstuhls für Neuro-Fuzzy-Systeme an der Universität Magdeburg.

Blurb

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