Abstract
This paper proposes the study of cluster analysis methods for solving the problem of pattern recognition, including group solution methods. The study selected methods for solving the problem of cluster analysis based on a group solution with incomplete training information, investigated and developed models of group solutions based on existing known algorithms. The novelty of the work consists in a combination of algorithms for collective cluster analysis and nuclear classification methods. Numerical experiments on test problems and a real hyperspectral image demonstrate the effectiveness of the proposed method, including in the presence of noisy data.