(untitled) (ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International Scientific and Practical Conference, 2015)
Classical clustering algorithms have been studied quite well, they are used for the numerical data grouping in similar structures - clusters. Similar objects are placed in the same cluster, different objects – in another cluster. All classical clustering algorithms have common characteristics, their successful choice defines the clustering results. The most important clustering parameters are following: clustering algorithms, metrics, the initial number of clusters, clustering validation criteria. In recent years there is a strong tendency of the possibility to get the rules from clusters. Semantic knowledge is not used in classical clustering algorithms. This creates difficulties in interpreting the results of clustering. Currently, the possibilities to use ontology increase rapidly, that allows to get knowledge of a specific data model. In the frames of this work the ontology concept, prototype development for numerical data clustering, which includes the most important characteristics of clustering performance have been analyzed.