Improving K-Modes Algorithm Considering Frequencies of Attribute Values in Mode

The original k-means algorithm is designed to work primarily on numeric data sets. This prohibits the algorithm from being applied to categorical data clustering, which is an integral part of data mining and has attracted much attention recently. The k-modes algorithm extended the k-means paradigm to cluster categorical data by using a frequency-based method to update the cluster modes versus the k-means fashion of minimizing a numerically valued cost. However, the dissimilarity measure used in k-modes doesn’t consider the relative frequencies of attribute values in each cluster mode, this will result in a weaker intra-cluster similarity by allocating less similar objects to the cluster. In this paper, we present an experimental study on applying a new dissimilarity measure to the k-modes clustering to improve its clustering accuracy. The measure is based on the idea that the similarity between a data object and cluster mode, is directly proportional to the sum of relative frequencies of the common values in mode. Experimental results on real life datasets show that, the modified algorithm is superior to the original kmodes algorithm with respect to clustering accuracy.