Computing approximate value of the pbm index for counting number of clusters using genetic algorithm

Abstract

Determining number of clusters present in a data set is an important problem in clustering. There exist very few techniques that can solve this problem satisfactorily. Most of these techniques are expensive with regard to computation time. Recently VAT (Visual Assessment of Tendency for clustering) images of data sets are used for this purpose along with GA and a validity index. A series of diagonal dark blocks in the VAT image represents possible clusters present in the data set. We shall show an efficient way to compute an approximate value of PBM index directly from the VAT image. It is shown that the present approach is able to suitable index values for finding appropriate number of dark blocks (clusters), under a GA framework.