Subject. The stability of regional economic systems in conditions of structural heterogeneity and the impact of external shocks. Objectives. Testing of a cross-algorithmic approach to assessing the sustainable development of the regions of the Siberian Federal District. Methods. The research is based on unsupervised machine learning methods, including K-means, DBSCAN, and Ward's hierarchical clustering algorithms. The empirical base is based on open statistical data characterizing the social, economic and environmental conditions of the studied regions. Data normalization and principal component analysis procedures were used to increase the level of correctness and interpretability. The cross-algorithmic approach is implemented through the comparison and mutual verification of cluster solutions obtained by various algorithms, as well as an analysis of their dynamics for 2019–2023. Results. As a result of the research, a stable three-level structure of the regional space of the Siberian Federal District has been revealed, which persists over time regardless of external changes. The analysis of socioeconomic and ecological profiles of the regions included in the corresponding cluster is carried out. It is shown that centroid and hierarchical methods reproduce the basic typology of regions, reflecting differences in the level and structure of development, while density clustering makes it possible to identify nonlinear effects, increased fragmentation, and atypical development trajectories. Conclusions. of the structural patterns of the regional space under changing conditions, and the cross-algorithmic approach increases the objectivity and reliability of assessing the sustainability of regional economic systems.
Keywords: sustainable development of regions, regional economic systems, machine learning, cluster analysis, cross-algorithmic approach
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