- Title
- Unsupervised and supervised data classification via nonsmooth and global optimisation
- Creator
- Bagirov, Adil; Rubinov, Alex; Sukhorukova, Nadezda; Yearwood, John
- Date
- 2003
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/42588
- Identifier
- vital:77
- Identifier
-
https://doi.org/10.1007/BF02578945
- Identifier
- ISSN:1134-5764
- Abstract
- We examine various methods for data clustering and data classification that are based on the minimization of the so-called cluster function and its modications. These functions are nonsmooth and nonconvex. We use Discrete Gradient methods for their local minimization. We consider also a combination of this method with the cutting angle method for global minimization. We present and discuss results of numerical experiments.; C1
- Publisher
- Sociedad de Estadistica Operativa
- Relation
- Top Vol. 11, no. 1 (2003), p. 1-92
- Rights
- Copyright Springer
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0102 Applied Mathematics; Optimisation; Clustering; Classification cluster function; Nonsmooth optimisation
- Full Text
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