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Density based fuzzy c-means clustering of non-convex patterns

Beliakov, Gleb and King, Matthew 2006, Density based fuzzy c-means clustering of non-convex patterns, European journal of operational research, vol. 173, no. 3, pp. 717-728.

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Title Density based fuzzy c-means clustering of non-convex patterns
Author(s) Beliakov, Gleb
King, Matthew
Journal name European journal of operational research
Volume number 173
Issue number 3
Start page 717
End page 728
Publisher North-Holland Pub. Co
Place of publication Amsterdam, Netherlands
Publication date 2006-09-16
ISSN 0377-2217
1872-6860
Keyword(s) data mining
non-linear programming
clustering
fuzzy c-means
non-smooth optimization
Summary We propose a new technique to perform unsupervised data classification (clustering) based on density induced metric and non-smooth optimization. Our goal is to automatically recognize multidimensional clusters of non-convex shape. We present a modification of the fuzzy c-means algorithm, which uses the data induced metric, defined with the help of Delaunay triangulation. We detail computation of the distances in such a metric using graph algorithms. To find optimal positions of cluster prototypes we employ the discrete gradient method of non-smooth optimization. The new clustering method is capable to identify non-convex overlapped d-dimensional clusters.


Language eng
Field of Research 010301 Numerical Analysis
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2005, Elsevier B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30003583

Document type: Journal Article
Collections: School of Engineering and Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.