The paper presents the simulation results of gene expression sequences clustering within the framework of the objective clustering inductive technology. As experimental data the gene expression sequences of lung cancer patients, which were obtained by microchip experiments, were used. The estimation of the grouping data quality was performed with the use of the internal and external clustering quality criteria on the two equal power subsets, which included the same quantity of pairwise similar objects. The final decision about the objects grouping is done based on the minimum value of the external clustering quality criterion.

Aim. The article presents the researches on the optimization of the DNA microarray data processing, which is aimed at improving the quality of object clustering. Methods. Data preprocessing was performed with program R using Bioconductor package. Modelling the clustering process was made in the software environment KNIME using the program WEKA functions. Results. The data preprocessing is shown to be optimal while using such techniques as the background correction rma method, quantile normalization, mas PM correction and summarization by mas method. The simulation results have demonstrated a high effectiveness of the clustering algorithm Sota for this category of data. Conclusion. The results of the research have shown that improving the quality of biological object clustering is possible by means of hybridization and optimization of the methods and algorithms at different stages of data processing.\u

Model of data clustering system based on the complex use of agglomerative hierarchical algorithm and inductive modeling methods of complex systems is presented in the paper. The quality of clustering is evaluated by two equal power subsets with the use of complex balance criterion, which takes into account both the displacement the mass centers of the appropriate clusters of different subsets and distribution of objects in the appropriate clusters relative to the mass center. Evaluating the effectiveness of the proposed model was performed using data sets containing clusters of different shapes. Charts of the external and internal complex criterion values against clustering level were created, which allows to determine the optimal clustering of a data set.