Classical clustering algorithms are sufficiently well
studied, they are used for grouping numerical data in similar
structures - clusters. Similar objects are placed in the same
cluster, different objects in another cluster. All of the classic
clustering algorithms have common parameters, and successful
selection of which also determines the clustering result. The
most important parameters characterizing clustering are: clus-
tering algorithm, metrics, initial number of clusters, criteria for
clustering accuracy. In recent years, there has been a tendency
towards the possibility of obtaining rules from clusters. Classical
clustering algorithms do not apply semantic knowledge. It creates
difficulties in interpreting the results of clustering. Presently, the
use of ontology opportunities is developing very rapidly, that
allows to gain knowledge about a certain data model. The paper
analyzes the concept of ontology and prototype development for
numerical data clusterization, which includes the most significant
indicators characterizing clusterization. The aim of the work is
to develop a concept for analyzing clustering data with the help
of ontologies. As a result of the work, a study has been conducted
on the use of ontologies in this type of tasks
