We examine various methods for data clustering and data classification that are based on the minimization of the so-called cluster function and its modifications. 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.
We study functions that can be represented as the sum of minima of convex functions. Minimization of such functions can be used for approximation of finite sets and their clustering. We suggest to use the local discrete gradient (DG) method [1] and the hybrid method between the cutting angle method and the discrete gradient method (DG+CAM) [5] for the minimization of these functions. We report and analyze the results of numerical experiments.
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