EFFICIENT SUBSPACE CLUSTERING FOR HIGHER DIMENSIONAL DATA USING FUZZY ENTROPY

EFFICIENT SUBSPACE CLUSTERING FOR HIGHER DIMENSIONAL DATA USING FUZZY ENTROPY

C.PALANISAMY; S.SELVAN

【期刊名称】《《系统科学与系统工程学报（英文版）》》 【年(卷),期】2009(018)001

【摘要】In this paper we propose a novel method for identifying relevant subspaces using fuzzy entropy and perform clustering. This measure discriminates the real distribution better by using membership functions for measuring class match degrees. Hence the fuzzy entropy reflects more information in the actual disbution of patterns in the subspaces. We use a heuristic procedure based on the silhouette criterion to find the number of clusters. The presented theories and algorithms are evaluated through experiments on a collection of benchmark data sets. Empirical results have shown its favorable performance in comparison with several other clustering algorithms. 【总页数】16页(95-110) 【关键词】

【作者】C.PALANISAMY; S.SELVAN

【作者单位】Department of Information Technology Bannari Amman Institute of Technology Sathyamangalam TN India; Department of Computer Science St. Peters Engineering College Chennai TN India 【正文语种】中文