On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm
Michael K. Ng, Mark Junjie Li, Joshua Zhexue Huang, Zengyou He · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2007
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.