Computation of Initial Modes for K-modes Clustering Algorithm Using Evidence Accumulation

Clustering accuracy of partitional clustering algorithm for categorical data depends primarily on the choice of initial data points to instigate the clustering process and hence the clustering results cannot be generated and repeated consistently. In this paper we present an approach to compute initial modes for K-mode partitional clustering algorithm to cluster categorical data sets. Here we utilized the idea of evidence accumulation for combining the results of multiple clusterings. Initially, n F − dimensional data is decomposed into a large number of compact clusters; the K-modes algorithm performs this decomposition, with several clusterings obtained by N random initializations of the K-modes algorithm and the modes thus obtained for every random initialization are stored in a Mode-Pool, PN. The objective is to investigate the contribution of those data objects / patterns that are less vulnerable to the choice of random selection of modes and to choose the most diverse set of modes from the available Mode-Pool that can be utilized as initial modes for the K-mode clustering algorithm. Experimentally we found that by this method we get initial modes that are very similar to the actual / desired modes and gives consistent and better clustering results with less variance of error than the traditional method of choosing random modes.