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Pattern Recognition with Fuzzy Objective Function Algorithms

Color image segmentation: advances and prospects

In this paper the convergence of a class of clustering procedures, popularly known as the fuzzy ISODATA algorithms, is established. The theory of Zangwill is used to prove that arbitrary sequences generated by these (Picard iteration) procedures always terminates at a local minimum, or at worst, always contains a subsequence which converges to a local minimum of the generalized least squares objective functional which defines the problem.

A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms

A survey of fuzzy clustering

Fuzzy sets in pattern recognition: methodology and methods

The fuzzy ISODATA algorithms are used to address two problems: first, the question of feature selection for binary valued data sets is investigated; and second, the same method is applied to the design of a fuzzy one-nearest prototype classifier The efficiency of this fuzzy classifier is compared to conventional k-NN classifiers by a computational example using the stomach disease data of Scheinok and Rupe, and Toussaint's method for estimation of the probability of misclassification: the fuzzy prototype classifier appears to decrease the error rate expected from all k-NN classifiers by roughly ten per cent

Patrick F. Castelaz

Papers

Prototype classification and feature selection with fuzzy sets