Data mining in terms of university staff clusterization based on scientometric indicators

Abstract

Data mining in terms of university staff clusterization based on scientometric indicators

Zyateva O.A., Pitukhin E.А., Peshkova I.V., Shabalina I.M.

Incoming article date: 10.12.2017

Data mining methods were used to analyze publication activity of the university staff on the example of the Petrozavodsk State University (hereinafter referred to as PetrSU). In order to identify employees groups with similar indicators of scientific activity, they were clustered. As a result, teaching staff was divided into eight clusters, three of which included employees representing both present and future of science at Petrozavodsk State University, and others that would strive to get into these groups. The presented results of indicators’ statistical processing can be useful for university self-analysis. The university management could draw a conclusion on a current state of scientific activity, both of an individual employee and of the organization as a whole. This will allow to make scientifically-based management decisions in order to improve scientific performance of the organization.

Keywords: university permormance, scientific activity, data mining, clustering, scientometric indicators, h-index, RSCI