A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification

In this article, the effectiveness of variable string length genetic algorithm along with a recently developed fuzzy cluster validity index (PBMF) has been demonstrated for clustering a data set into an unknown number of clusters. The flexibility of a variable string length Genetic Algorithm (VGA) is utilized in conjunction with the fuzzy indices to determine the number of clusters present in a data set as well as a good, fuzzy partition of the data for that number of clusters. A comparative study has been performed for different validity indices, namely, PBMF, XB, PE and PC. The results of the fuzzy VGA algorithm are compared with those obtained by the well known FCM algorithm which is applicable only when the number of clusters is fixed a priori. Moreover, another genetic clustering scheme, that also requires fixing the value of the number of clusters, is implemented. The effectiveness of the PBMF index as the optimization criterion along with a genetic fuzzy partitioning technique is demonstrated on a number of artificial and real data sets including a remote sensing image of the city of Kolkata. (c) 2005 Elsevier B.V. All rights reserved.