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On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm
March 2007 (vol. 29 no. 3)
pp. 503-507
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.
Index Terms:
Data mining, clustering, k-modes algorithm, categorical data.
Citation:
Michael K. Ng, Mark Junjie Li, Joshua Zhexue Huang, Zengyou He, "On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 3, pp. 503-507, March 2007, doi:10.1109/TPAMI.2007.53
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