Fuzzy clustering with genetic search

Abstract

Pattern classification task consists of clustering the training samples into known classes and using these clusters to classify new samples. Clustering is done by finding an appropriate set of ellipsoids for enclosing each of the classes. To obtain fuzzy clustering, membership values are assigned to samples against ellipsoids of all classes and these values are defuzzified for final classification. During the clustering phase, a variant of genetic algorithms, which allows variable-length genotypes, is employed in searching for the set of ellipsoids for all the classes. In particular, the number of clusters is not assumed to be known a priori, and is, in effect, determined by the genetic search dynamically. The evaluation function drives the search towards a set of ellipsoids which maximizes the correctness of classification of the training samples while having minimum total volume.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>