Model of the objective clustering inductive technology of gene expression profiles based on SOTA and DBSCAN clustering algorithms

Abstract

The paper presents the hybrid model of the objective clustering inductive technology based on complex using of the self-organizing SOTA and the density DBSCAN clustering algorithms. The inductive methods of complex systems analysis were used as the basis to implement the objective clustering inductive technology of gene expression profiles. To estimate the clustering quality for equal power subsets (include the same quantity of pairwise similar objects) the complex multiplicative criterion was calculated as the combination of the Calinski-Harabasz criterion and WB-index. The external clustering quality criterion is calculated as the normalized difference of the internal clustering quality criteria for the equal power subsets. The final decision concerning the determination of the optimal parameters of the clustering algorithm operation is done based on the maximum value of the Harrington desirability function that takes into account both the character of the objects and the clusters distribution in various clustering and the difference between clustering, which are implemented on the equal power subsets. The studied data grouping within the framework of the objective clustering inductive technology was performed in two stages. Firstly, the studied gene expression profiles were grouped with the use DBSCAN clustering algorithm. Then, the obtained set of gene expression profiles was divided into two clusters using SOTA clustering algorithm. This step-by-step procedure of the data clustering crates the conditions to save more useful information for following data processing.