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On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm

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4 Author(s)
Ng, M.K. ; Dept. of Math., Hong Kong Baptist Univ., Kowloon ; Li, M.J. ; Huang, J.Z. ; Zengyou He

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 (Z. He, et al., 2005), (O. San, et al., 2004) 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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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:29 ,  Issue: 3 )