A stochastic game model of data clustering under interference conditions is proposed. An adaptive recurrent method and algorithm for stochastic game deciding have developed. Computer simulation of game clustering of noisy data has performed. The parameters influence on the stochastic game method convergence for noisy data clustering is researched. For this purpose, each data point is considered as a separate player with the ability to learn and adapt to the uncertainties of the system. After

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... selection of clusters is completed, all players calculate the corresponding losses by the criteria of minimizing the total distance between the cluster points formed by the free choice of player strategies. The results obtained are analysed. A stochastic approximation based on the mixed-strategy adjustment method minimizes the mean loss functions on single simplexes.