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Improving categorical data clustering algorithm by weighting uncommon attribute value matches

He Zengyou (Department of Computer Science and Engineering, Harbin Institute of Technology, China)
Xu Xiaofei (Department of Computer Science and Engineering, Harbin Institute of Technology, China)
Deng Shenchun (Department of Computer Science and Engineering, Harbin Institute of Technology, China)

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.