Federation Subjects' Import Dependence Level Hierarchical Clustering Model

The paper proposes Russian regions clustering approach by the level of their import dependence. Difficultly formalized problem solution regarding clustering regions by theirs import dependence level performed using the hierarchical clustering mathematical apparatus based on Russian Federation Federa...

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Bibliographic Details
Published in2023 IV International Conference on Neural Networks and Neurotechnologies (NeuroNT) pp. 25 - 28
Main Authors Mityakov, Evgenii S., Mityakov, Sergei N., Murashova, Natalia A., Ladynin, Andrey I., Shmeleva, Nina M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.06.2023
Subjects
cluster analysis
Clustering algorithms
Computational modeling
dendrogram
Economics
Food products
hierarchical clustering
import dependence
import substitution
machine learning
Metals
Neural networks
Raw materials
Russian Federation regions
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DOI10.1109/NeuroNT58640.2023.10175840

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Summary:The paper proposes Russian regions clustering approach by the level of their import dependence. Difficultly formalized problem solution regarding clustering regions by theirs import dependence level performed using the hierarchical clustering mathematical apparatus based on Russian Federation Federal State Statistics Service data. In order to form clustering criteria system, official statistical data on the country's foreign economic activity commodity structure were selected for 6 groups: food products and agricultural raw materials; fuel and energy complex products; chemical industry products; wood, pulp and paper products; metals and makings; machines, equipment and vehicles. Clustering algorithm reasonable choice was carried out in the work. Volga and Central federal districts computational experiment was conducted in order to clearly demonstrate import substitution level.
DOI:10.1109/NeuroNT58640.2023.10175840