A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm
Hongfang Zhou1, Yihui Zhang1, Yibin Liu1
1School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
Abstract:
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.
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