A genetic k-modes algorithm for clustering categorical data

Many optimization based clustering algorithms suffer from the possibility of stopping at locally optimal partitions of data sets. In this paper, we present a genetic kappa-Modes algorithm(GKMODE) that finds a globally optimal partition of a given categorical data set into a specified number of clusters. We introduce a k-Modes operator in place of the normal crossover operator. Our analysis shows that the clustering results produced by CKMODE are very high in accuracy and it performs much better than existing algorithms for clustering categorical data.