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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 3691316, 7 pages
Research Article

A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm

School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China

Correspondence should be addressed to Hongfang Zhou

Received 19 January 2017; Revised 4 March 2017; Accepted 19 March 2017; Published 28 March 2017

Academic Editor: Elio Masciari

Copyright © 2017 Hongfang Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The k-modes clustering algorithm has been widely used to cluster categorical data. In this paper, we firstly analyzed the k-modes algorithm and its dissimilarity measure. Based on this, we then proposed a novel dissimilarity measure, which is named as GRD. GRD considers not only the relationships between the object and all cluster modes but also the differences of different attributes. Finally the experiments were made on four real data sets from UCI. And the corresponding results show that GRD achieves better performance than two existing dissimilarity measures used in k-modes and Cao’s algorithms.