Pattern Recognition with Fuzzy Objective Function Algorithms

Name: Pattern Recognition with Fuzzy Objective Function Algorithms
Brand: Springer-Verlag New York Inc.
SKU: 9781475704525
Price: 115.72 EUR
Availability: InStock

Автор: James C. Bezdek

Език:

Корици: С меки корици

Издател: Springer-Verlag New York Inc.

Наличност: Външен склад

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The fuzzy set was conceived as a result of an attempt to come to grips with the problem of pattern r...

Информация за книгата

Автор

James C. Bezdek

Език

Английски език

Корици

Книга - С меки корици

Издадена

2012

страници

272

EAN

9781475704525

ISBN

9781475704525

Enbook ID

06618140

Издател

Springer-Verlag New York Inc.

Теглоt

394

Размери

228 x 153 x 16

Пълно описание

The fuzzy set was conceived as a result of an attempt to come to grips with the problem of pattern recognition in the context of imprecisely defined categories. In such cases, the belonging of an object to a class is a matter of degree, as is the question of whether or not a group of objects form a cluster. A pioneering application of the theory of fuzzy sets to cluster analysis was made in 1969 by Ruspini. It was not until 1973, however, when the appearance of the work by Dunn and Bezdek on the Fuzzy ISODATA (or fuzzy c-means) algorithms became a landmark in the theory of cluster analysis, that the relevance of the theory of fuzzy sets to cluster analysis and pattern recognition became clearly established. Since then, the theory of fuzzy clustering has developed rapidly and fruitfully, with the author of the present monograph contributing a major share of what we know today. In their seminal work, Bezdek and Dunn have introduced the basic idea of determining the fuzzy clusters by minimizing an appropriately defined functional, and have derived iterative algorithms for computing the membership functions for the clusters in question. The important issue of convergence of such algorithms has become much better understood as a result of recent work which is described in the monograph.

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