Unsupervised and supervised data classification via nonsmooth and global optimization |
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Authors: | A M Bagirov A M Rubinov N V Soukhoroukova J Yearwood |
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Institution: | (1) School of Information Technology and Mathematical Sciences, The University of Ballarat, 3353, Vic, Australia |
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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.
This research was supported by the Australian Research Council. |
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Keywords: | Clustering classification cluster function nonsmooth optimization global optimization |
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