Unsupervised and supervised data classification via nonsmooth and global optimization

@article{Bagirov2003UnsupervisedAS,
title={Unsupervised and supervised data classification via nonsmooth and global optimization},
author={A. Bagirov and A. M. Rubinov and N. Soukhoroukova and J. Yearwood},
journal={Top},
year={2003},
volume={11},
pages={1-75}
}

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.