Attribute value weighting in k-modes clustering

Authors:
Zengyou He;Xiaofei Xu;Shengchun Deng
Affiliations:
School of Software, Dalian University of Technology, China;Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China;Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In this paper, we generalize the k-modes clustering algorithm by weighting attribute value in the dissimilarity computation. Such a generalization generates clusters with stronger intra-similarities, leading to better clustering performance. Experimental results on real life data show that the new k-modes algorithm is superior to the standard k-modes algorithm with respect to clustering accuracy.