alexa On the impact of dissimilarity measure in k-modes clustering algorithm.
Mathematics

Mathematics

Journal of Applied & Computational Mathematics

Author(s): Ng MK, Li MJ, Huang JZ, He Z

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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. This article was published in IEEE Trans Pattern Anal Mach Intell and referenced in Journal of Applied & Computational Mathematics

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