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A spatially constrained fuzzy hyper-prototype clustering algorithm
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangshu, China; School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia.
School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia.ORCID iD: 0000-0002-4255-5130
2012 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 45, no 4, 1759-1771 p.Article in journal (Refereed) PublishedText
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2012. Vol. 45, no 4, 1759-1771 p.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-125030DOI: 10.1016/j.patcog.2011.11.001ISI: 000300459000043OAI: oai:DiVA.org:liu-125030DiVA: diva2:902761
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2016-02-22Bibliographically approved

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Pham, Tuan D
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