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Fuzzy hyper-prototype clustering
School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, 2600, Australia .
School of Engineering and Information Technology University of New South Wales Canberra, ACT 2600, Australia.ORCID iD: 0000-0002-4255-5130
2010 (English)In: Knowledge-Based and Intelligent Information and Engineering Systems: 14th International Conference, KES 2010, Cardiff, UK, September 8-10, 2010, Proceedings, Part I / [ed] Rossitza Setchi, Ivan Jordanov, Robert J. Howlett, Lakhmi C. Jain, Springer Berlin/Heidelberg, 2010, 6276, p. 379-389Chapter in book (Other academic)
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Abstract [en]

We propose a fuzzy hyper-prototype algorithm in this paper. This approach uses hyperplanes to represent the cluster centers in the fuzzy c-means algorithm. We present the formulation of a hyperplanebased fuzzy objective function and then derive an iterative numerical procedure for minimizing the clustering criterion. We tested the method with data degraded with random noise. The experimental results show that the proposed method is robust to clustering noisy linear structure.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2010, 6276. p. 379-389
Series
Lecture Notes in Computer Science, ISSN 0302-9743
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-127947DOI: 10.1007/978-3-642-15387-7_42Libris ID: 12034963ISBN: 978-3-642-15386-0 (print)ISBN: 978-3-642-15387-7 (print)OAI: oai:DiVA.org:liu-127947DiVA: diva2:928898
Available from: 2016-05-17 Created: 2016-05-13 Last updated: 2018-01-26Bibliographically approved

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Pham, Tuan D

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Computer Vision and Robotics (Autonomous Systems)

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Citation style
  • apa
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  • Other style
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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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