On the application of one approach for data clustering in the agro-industrial complex

Abstract

The paper presents an approach to the automatic grouping algorithms development based on parametric optimization models for processing high-volume data in the agrarian and industrial complex. Combined search algorithms with alternating randomized neighborhoods show much more stable results (give a smaller minimum value, and also have a low standard deviation of the target function) and hence better performance compared to known (so-called classical) algorithms, such as j-means and k-means.