On metric clustering

Using the combinatorial-logical approach, we consider the problem of clustering a set of vectors from an $n$-dimensional vector space of attributes. The main results of the paper are the study of the optimal partitions into clusters with respect to some functionals and the design of iterative and gradient algorithms of clustering based on this consideration. It is shown that all optimal partitions are well-separable with respect to the corresponding decision rules.