UDC 004.421.2

Improving Categorical Data Clustering Algorithm by Weighting Uncommon Attribute Value Matches

Zengyou He1, Xiaofei Xu1 and Shenchun Deng1

  1. Department of Computer Science and Engineering, Harbin Institute of Technology
    92 West Dazhi Street, P.O Box 315, China, 150001
    zengyouhe@yahoo.com, {xiaofei,dsc}@hit.edu.cn

Abstract

This paper presents an improved Squeezer algorithm for categorical data clustering by giving greater weight to uncommon attribute value matches in similarity computations. Experimental results on real life datasets show that, the modified algorithm is superior to the original Squeezer algorithm and other clustering algorithm with respect to clustering accuracy.

Publication information

Volume 3, Issue 1 (Jun 2006)
Year of Publication: 2006
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

He, Z., Xu, X., Deng, S.: Improving Categorical Data Clustering Algorithm by Weighting Uncommon Attribute Value Matches. Computer Science and Information Systems, Vol. 3, No. 1, 23-32. (2006)