Implementation of the Objective Clustering Inductive Technology Based on DBSCAN Clustering Algorithm

The paper presents the results of the research of the clustering algorithm DBSCAN practical implementation within the framework of the objective clustering inductive technology. As experimental, the data Aggregation and Compound of the Computing school of the East Finland University and the gene expression sequences of lung cancer patients of the database ArrayExpres were used. The architecture of the objective clustering model has been developed. The implementation of the model involves the parallel data clustering on the two equal power subsets, which include the same quantity of pairwise similar objects. The choice of the solution about parameters of the algorithm determination has been carried out based on the minimum value of the external clustering quality criterion, which calculated as normalized difference of the internal clustering quality criteria for the two subsets.