Unsupervised Pattern Classification using Genetic Algorithms

Abstract

This article deals with the development of an unsupervised pattern classification technique that exploits the searching capability of genetic algorithms for automatically clustering a given data set into an appropriate number of clusters. Since the number of clusters is not known a priori, a modified string representation, comprising both real numbers and the don't care symbols, is used in order to encode a variable number of clusters. The Dunn's index is used as a measure of the fitness of a chromosome. Effectiveness of the genetic clustering scheme is demonstrated for several artificial and real-life data sets with the number of dimensions ranging from two to nine, and the number of clusters ranging from two to six.