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Pattern Recognition with Fuzzy Objective Function Algorithms (Paperback, Softcover reprint of the original 1st ed. 1981)
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Pattern Recognition with Fuzzy Objective Function Algorithms (Paperback, Softcover reprint of the original 1st ed. 1981)
Series: Advanced Applications in Pattern Recognition
Expected to ship within 10 - 15 working days
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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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