Affiliations: School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia | [x] University of South Australia, Adelaide, South Australia, Australia | [y] University of Science, Malaysia
Abstract: We propose a fuzzy hyper-prototype clustering algorithm in this paper. This approach uses hyperplanes to represent the cluster centers in the fuzzy clustering. We present the formulation of fuzzy objective function and derive an iterative numerical algorithm for minimizing the objective function. Validations and comparisons are made between the proposed fuzzy clustering algorithm and existing fuzzy clustering methods on artificially generated data as well as on real world dataset include UCI dataset and gene expression dataset, the results show that the proposed method can give better performance in the above cases.
