A Creditable Subspace Labeling Method Based on D-S Evidence Theory

Abstract

Due to inherent sparse, noise and nearly zero difference characteristics of high dimensional data sets, traditional clustering methods fails to detect meaningful clusters in them. Subspace clustering attempts to find the true distribution inherent to the subsets with original attributes. However, which subspace contains the true clustering result is usually uncertain. From this point of view, subspace clustering can be regarded as an uncertain discursion problem. In this paper, we firstly develop the criterion to evaluate creditable subspaces which contain the meaningful clustering results, and then propose a creditable subspace labeling method (CSL) based on D-S evidence theory. The creditable subspaces of the original data space can be found by iteratively executing the algorithm CSL. Once the creditable subspaces are got, the true clustering results can be found using a traditional clustering algorithm on each creditable subspace. Experiments show that CSL can detect the actual creditable subspace with the original attribute. In this way, a novel approach of clustering problems using traditional clustering algorithms to deal with high dimension data sets is proposed.

Supported by the Nation Science Foundation of China under Grand No.90412007 , the Nation Science Foundation of China No.60503003, the Science Research Project of AnHui Education office No KJ2008B133, and the important Science Research Project of AnHui Education office NO KJ2007A072.