Author:
Rozhnov Ivan P.,Kazakovtsev Lev A.,Karaseva Margarita V.,Rezova Natalya L.,Gaiduk Igor A.
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.
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