Application of clustering methods in the economic analysis of regions

Technological and digital transformation, penetrating all economies, makes demands on the social infrastructure readiness level. The purpose of the article is to evaluate regions’ education infrastructure readiness for digital transformation, to identify the differentiation of regions of the Russian Federation using machine learning methods. Clustering of the Russian Federation regions was carried out according to the degree of digital equipment of educational institutions. Clustering was carried out using self-organizing Kohonen maps and classical clustering methods, in particular the Linkage method. The results obtained showed the possibility of distributing the regions into four uneven clusters that have significant differences. It is noted that in the regions of the Russian Federation, the organizations carrying out educational activities in higher education programs have the highest computer availability, while the schools have the lowest one. These studies can be used for further analysis and decision-making that increase the efficiency of digital development of the regions of the Russian Federation, including for the search for economic solutions in the field of infrastructure development.

Keywords: digital transformation, clustering, differentiation of regions, education infrastructure, machine learning, neural networks, self-organizing Kohonen maps

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