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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
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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