Classification tasks are among the most important both in scientific and technical research conducted in biology, medicine, geology, and in socio-economic ones. In terms of their formulation, such tasks are diverse and numerous. One of them - classification without training - consists in dividing a set of objects that are described by a set of features into homogeneous groups called clusters. To solve such a problem, cluster analysis methods are used, which make it possible to single out clusters in the p-dimensional space of features of multidimensional objects of a very different nature that have special properties (compactness, connectivity, and others). When classifying multidimensional observations, the results of cluster analysis allow the researcher to more reasonably attribute an unknown object to one or another known class.
