FHC: The fuzzy hyper-prototype clustering algorithm

Issue title: Intelligent Information Processing: Techniques and Applications

Guest editors: C.P. Limx and L.C. Jainy

Article type: Research Article

Authors: Liu, Jin^{; *} | Pham, Tuan D.

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

Correspondence: [*] Corresponding author. E-mail: j.liu@student.adfa.edu.au

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.

Keywords: FHC, fuzzy clustering algorithm, hyper-prototype, pattern classification

DOI: 10.3233/KES-2012-0231

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 16, no. 1, pp. 35-47, 2012