A spatially constrained fuzzy hyper-prototype clustering algorithm

Authors:
Jin Liu;Tuan D. Pham
Affiliations:
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangshu 221008, China;School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT 2600, Australia
Venue:
Pattern Recognition
Year:
2012

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Abstract

We present in this paper a fuzzy clustering algorithm which can handle spatially constraint problems often encountered in pattern recognition. The proposed method is based on the notions of hyperplanes, the fuzzy c-means, and spatial constraints. By adding a spatial regularizer into the fuzzy hyperplane-based objective function, the proposed method can take into account additionally important information of inherently spatial data. Experimental results have demonstrated that the proposed algorithm achieves superior results to some other popular fuzzy clustering models, and has potential for cluster analysis in spatial domain.