Zengyou He¹, Xiaofei Xu¹, 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.