beliakov-fuzzyclusteringnon-convex-2001.pdf (227.96 kB)
Fuzzy clustering of non-convex patterns using global optimization
This paper discusses various extensions of the classical within-group sum of squared errors functional, routinely used as the clustering criterion. Fuzzy c-means algorithm is extended to the case when clusters have irregular shapes, by representing the clusters with more than one prototype. The resulting minimization problem is non-convex and non-smooth. A recently developed cutting angle method of global optimization is applied to this difficult problem
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Title of proceedings
Meeting the grand challenge : machines that serve people : [proceedings of] the 10th IEEE International Conference on Fuzzy Systems, December 2001, 2-5 December, the University of Melbourne, AustraliaEvent
IEEE International Conference on Fuzzy Systems (10th : 2001 : Melbourne, Australia)Pagination
220 - 223Publisher
IEEELocation
University of MelbournePlace of publication
Melbourne, VicStart date
2001-12-02End date
2001-12-05ISBN-13
9780780372931ISBN-10
078037293XLanguage
engNotes
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Publication classification
E1 Full written paper - refereedCopyright notice
2001, IEEEEditor/Contributor(s)
Y HongUsage metrics
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