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On the impact of dissimilarity measure in k-modes clustering algorithm
Michael K Ng1, Mark Junjie Li, Joshua Zhexue Huang
1Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong. mng@math.hkbu.edu.hk
Abstract:
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.
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