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RESEARCH CENTRE FOR INDUSTRIAL DEVELOPMENT PROBLEMS of NAS of Ukraine (KHARKIV, UKRAINE)

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Liburkina L. M.

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Methods of Multidimensional Classification of Economic Objects as a Tool For Analyzing Metaspatial Business Collaborations in the Trade Sector
Chernova N. L., Chernov O. O., Dyachkova O. V.

Chernova, Natalia L., Chernov, Oleksandr O., and Dyachkova, Olga V. (2026) “Methods of Multidimensional Classification of Economic Objects as a Tool For Analyzing Metaspatial Business Collaborations in the Trade Sector.” Business Inform 4:202–212.
https://doi.org/10.32983/2222-4459-2026-4-202-212

Section: Economic and Mathematical Modeling

Article is written in Ukrainian
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UDC 334.7, 330.4

Abstract:
The article examines the possibilities of applying methods of multidimensional classification of economic objects for analyzing metaspatial business collaborations in the trade sector in the context of economic digitalization. It is substantiated that modern business collaborations are formed within complex network structures, where enterprises with different characteristics interact, which requires the use of comprehensive analytical approaches. The features of forming a system of indicators for evaluating the activities of economic objects are considered, covering financial, market, and structural characteristics and presented in various types of measurement scales. It is determined that data heterogeneity complicates the application of classical economic and mathematical methods and requires the use of specialized approaches to assessing similarity between objects. The feasibility of using generalized similarity measures for mixed data has been substantiated, in particular the Gower’s distance, which allows integrating quantitative, ordinal, and nominal features into a single analytical model. On this basis, the classification of economic entities was implemented using the k-medoids algorithm, which ensures correct handling of heterogeneous data and increases the robustness of results against outliers. As a result of the empirical study, the optimal number of clusters was determined, and their profiles were formed, which made it possible to identify groups of enterprises with different levels of financial performance, market position, and risk. It was shown that using medoids as typical representatives of clusters significantly simplifies the interpretation of results and increases their practical value. It has been proved that the proposed approach allows for the identification of potentially compatible groups of companies for the formation of efficient business collaborations, as well as contributing to a deeper understanding of the structure of metaspacial interactions in the field of trade. The obtained results can be used to support management decisions and the development of partnership strategies in the digital economy.

Keywords: multidimensional classification; economic entities; metaspacial business collaborations; trade; cluster analysis; k-medoids; Gower’s distance; mixed data; similarity measures; digital economy.

Fig.: 8. Tabl.: 5. Formulae: 11. Bibl.: 18.

Chernova Natalia L. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Software Engineering and Intelligent Control Technologies, National Technical University «Kharkiv Polytechnic Institute» (2 Kyrpychova Str., Kharkіv, 61002, Ukraine)
Email: [email protected]
Chernov Oleksandr O. – Postgraduate Student, Department of Entrepreneurship, Trade and Logistics, National Technical University «Kharkiv Polytechnic Institute» (2 Kyrpychova Str., Kharkіv, 61002, Ukraine)
Email: [email protected]
Dyachkova Olga V. – Associate Professor, Department of Economic Cybernetics and Applied Economics, V. N. Karazin Kharkiv National University (4 Svobody Square, Kharkіv, 61022, Ukraine)
Email: [email protected]

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