High Dimensional Clustering Algorithm Based on Local Significant Units
Zong Yu①② Li Ming-chu① Xu Guan-dong② Zhang Yan-chun②
①(School of Software, Dalian University of Technology, Dalian 116621, China) ②(Center of Applied Information, Victoria University, Melbourne VIC3011, Australia)
Abstract：High dimensional clustering algorithm based on equal or random width density grid cannot guarantee high quality clustering results in complicated data sets. In this paper, a High dimensional Clustering algorithm based on Local Significant Unit (HC_LSU) is proposed to deal with this problem, based on the kernel estimation and spatial statistical theory. Firstly, a structure, namely Local Significant Unit (LSU) is introduced by local kernel density estimation and spatial statistical test; secondly, a greedy algorithm named Greedy Algorithm for LSU (GA_LSU) is proposed to quickly find out the local significant units in the data set; and eventually, the single-linkage algorithm is run on the local significant units with the same attribute subset to generate the clustering results. Experimental results on 4 synthetic and 6 real world data sets showed that the proposed high-dimensional clustering algorithm, HC_LSU, could effectively find out high quality clustering results from the highly complicated data sets.
2010, Vol. 32 
