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A spatially constrained fuzzy hyper-prototype clustering algorithm
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DOI：10.1016/j.patcog.2011.11.001.png)
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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. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Fuzzy c-means
Fuzzy hyper-prototype clustering
Spatial models
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IF:
7.6
论文数:
1.3W
被引数:
4.5W
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