Methods of Sustainable Clustering of Russian Regions by Employment

The problem of the imbalance in labor market Russian Federation cannot be solved without leveling heterogeneity its regions by socio-economic and demographic characteristics, since is a dynamic complex system that influenced variety factors, such as economic, situation, quality education, interests participants, technological progress digitalization, psychological aspects, etc. article discusses application cluster discriminant analysis methods on data, highlights regional features Russia. Cluster was carried out using traditional hierarchical iterative methods: “Nearest Neighbor” method, “Far “Ward” method k-means well fanny fuzzy clustering method. results obtained these five were evaluated for consistency. conducted allowed us to obtain stable structure terms number employed people type economic activity, dividing Russia into four main groups characterized positive, average, neutral negative behavior. Thanks construction profiles clusters, poorly informative types activity identified, employment which has little effect division groups. evaluates errors final clustering. with high low levels are analyzed, atypical subjects identified their industry specialization considered. A comparative formed out, can conditionally assigned any identified. typologization been developed taking account territorial, social, sectoral climatic features.