Data Mining for the Decision Support System of the Expert Scientific Council in the Field of Higher Education

This study proposes using modern data mining technologies to create a decision support system for an expert scientific commission of a higher educational institution. The methodology for calculating standard values of scientometric indicators is examined. An attempt is being made to create a self-organizing system in the teaching staff, striving to avoid both excessive workload and a decrease in the quality of the educational process. Using the method of cluster data analysis, it is proposed to divide the teaching staff into “workload” groups according to the similarity of the following characteristics: position held, the amount of teaching and methodological workload, the degree of influence on the process of generating scientific ideas (co-authorship significance). It is proposed to establish its own normative intervals of scientometric indicators for each selected cluster, allowing the achievements of the majority of participants in the study group to be considered successful.