|
|
|
| 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.
|
|
Received: 11 December 2009
|
|
|
|
Corresponding Authors:
Zong Yu
E-mail: nick.zongy@gmail.com
|
|
|
|
|
|
|
2010, Vol. 32 
