Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Abstract

Fuzzy techniques have been used for handling vague boundaries of arbitrarily oriented cluster structures. However, traditional clustering algorithms tend to break down in high dimensional spaces due to inherent sparsity of data. In order to model the uncertainties of high dimensional data, we propose modification of objective functions of Gustafson Kessel algorithm for subspace clustering, through automatic selection of weight vectors and present the results of applying the proposed approach to UCI data sets. © 2009 Springer-Verlag Berlin Heidelberg.