Genetic clustering for automatic evolution of clusters and application to image classification

In this article the searching capability of genetic algorithms has been exploited for automatically evolving the number of clusters as well as Proper Clustering of any data set. A new string representation, comprising both real numbers and the do not care symbol, is used in order to encode a variable number of clusters. The Davies-Bouldin index is used as a measure of the validity of the clusters. Effectiveness of the genetic clustering scheme is demonstrated for both artificial and real-life data sets. Utility of the genetic clustering technique is also demonstrated for a satellite image of a part of the city Calcutta. The proposed technique is able to distinguish some characteristic landcover types in the image. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.