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The Data Mining Technology Applications for Modeling the Unevenness of Socio-Economic Development of Regions
Chagovets L. O., Chahovets V. V., Didenko A. S.

Chagovets, Liubov O., Chahovets, Vita V., and Didenko, Anastasia S. (2020) “The Data Mining Technology Applications for Modeling the Unevenness of Socio-Economic Development of Regions.” Business Inform 3:82–91.
https://doi.org/10.32983/2222-4459-2020-3-82-91

Section: Economic and Mathematical Modeling

Article is written in Ukrainian
Downloads/views: 1

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UDC 519.86:330.34(1-32)

Abstract:
The article is aimed at developing models for reduction of the information space for the assessment of the socio-economic development of Ukrainian regions (SER), as well as allocating the factor groups of indicators that are of particular importance for improving the efficiency of formation and decision-making in the elaboration of the development strategies of regions. The carried out monograph analysis, systemization and generalization of modern scientific developments of domestic and foreign scholars allowed to determine the existence of a number of approaches to the definition and evaluation of the uneven socio-economic development of regions and the absence of a single base of assessment indicators of the SER. As a result of the study, a model to reduce indicators for evaluating the uneven socio-economic development of Ukrainian regions by Data Mining methods is developed, in particular by the principal components method, which allows to significantly narrow the system of estimates. As a result of the carried out modeling, the degree of influence and load of uneven indicators in the context of individual factor groups of the SER indicators in accordance with their principal components is determined. Prospects for further research in this direction should be the development of models for the classification of the SER statuses and forecasting the level of unevenness of the SER on the basis of the proposed complex of models for assessing the asymmetry and unevenness of regional development; forming a system of directions and the most significant strategic levers of regional development. The obtained results will allow to scale models according to the data of the European Union countries, which will serve as a basis for improving models of equalization of the asymmetry of the macro-regions development.

Keywords: model, modeling, region, Data Mining, Data Science, factor analysis, principal components method.

Fig.: 8. Formulae: 3. Bibl.: 24.

Chagovets Liubov O. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Economic Cybernetics, Simon Kuznets Kharkiv National University of Economics (9a Nauky Ave., Kharkiv, 61166, Ukraine)
Email: [email protected]
Chahovets Vita V. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Economics and Management, Kharkiv State University of Food Technology and Trade (333 Klochkіvska Str., Kharkіv, 61051, Ukraine)
Email: [email protected]
Didenko Anastasia S. – Specialist, Company Intetics Inc. (43a Nauky Ave., Kharkiv, 61072, Ukraine)

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