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This correspondence describes extensions to the k-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in [4], [12] which allows the use of the k-modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k-modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework.
Data mining, clustering, k-modes algorithm, categorical data.

Z. He, J. Z. Huang, M. J. Li and M. K. Ng, "On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 29, no. , pp. 503-507, 2007.
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