Fuzzy clustering of non-convex patterns using global optimization
Beliakov, Gleb 2001, Fuzzy clustering of non-convex patterns using global optimization, in 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, Australia, IEEE, Melbourne, Vic, pp. 220-223.
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
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.
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.
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 firstname.lastname@example.org.
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 email@example.com.