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Clustering of multidimensional temporal data as part of information support for management decisions

https://doi.org/10.32362/2500-316X-2026-14-2-7-16

EDN: VCTFHE

Abstract

Objectives. The aim of information support for management decision-making is to find the most optimal option. Cluster analysis of multivariate data characterizing socioeconomic systems is widely used. In this work, the author aims to increase the efficiency of decisions made to manage these systems based on the clustering of temporal multidimensional data.

Methods. The methods of cluster analysis were used, as well as the provisions of the theory of systems and mathematical statistics.

Results. A methodology for analyzing the functioning of socioeconomic systems was developed. The analysis is implemented in three stages. Firstly, clustering over the values of feature variances was applied. Secondly, the distance of clustering objects from the center of their cluster and their dispersion was calculated at the points of time coordinates. Thirdly, the change in belonging to a certain cluster of objects that came into view earlier was monitored. Unstable systems were then identified.

Conclusions. Two cases were considered to justify the effectiveness of the methodology developed herein. First, using the example of the tax administration, the detection of deliberate distortion of information was considered. Secondly, identifying the abnormal functioning of the regions of the Russian Federation using the example of decision-making in the framework of socioeconomic development management was considered. The analysis demonstrated good results and we can thus recommend the proposed methodology for practical use in information systems for supporting management decisions.

About the Author

M. A. Anfyorov
MIREA – Russian Technological University
Russian Federation

Mikhail A. Аnfyorov, Dr. Sci. (Eng.), Professor, Department of Domain-Specific Information Systems, Institute for Cybersecurity and Digital Technologies


Competing Interests:

The author declares no conflicts of interest.



References

1. Anfyorov M.A. Genetic clustering algorithm. Russian Technological Journal. 2019;7(6):134–150 (in Russ.). https://doi.org/10.32362/2500-316X-2019-7-6-134-150

2. Zamyatina E.E. Clustering of constituent entities of the Russian Federation by the level of development of creative industries. Progressivnaya ehkonomika = Progressive Economy. 2024;9:113–128 (in Russ.). https://doi.org/10.54861/27131211_2024_9_113

3. Protasov Yu.M., Yurov V.M. Clusterization of the Regions of the Russian Federation by their Level Socio-Economic Development. Vestnik Moskovskogo gosudarstvennogo oblastnogo universiteta. Seriya Ehkonomika = Bulletin of Moscow Region State University. Series: Economicsi. 2022;2:95–103 (in Russ.). https://doi.org/10.18384/2310-6646-2022-2-95-103

4. Аnfyorov M.А., Rashitova O.B. SADT modeling of the taxation system in the Russian Federation. Ehkonomika i upravlenie: nauchno-prakticheskii zhurnal = Economics and Management: Research and Practice Journal. 2015;124(2):94–101 (in Russ.). https://www.elibrary.ru/tqjqfj

5. Vylkova E.S., Viktorova N.G., Naumov V.N., Pokrovskaya N.V. Tax clusterization of regions of the Russian Federation to identify territories-drivers of sustainable development. Vestnik Tomskogo gosudarstvennogo universiteta. Ehkonomika = Tomsk State University Journal of Economics. 2021;53:138–157 (in Russ.). https://doi.org/10.17223/19988648/53/11

6. Greenberg G.M., Nikolaeva Y.S., Hegay L.B. Cluster approach to development of electronic educational resources for students of the technical university. In: Reshetnev Readings: Proceedings of the 25th International Scientific and Practical Conference. Krasnoyarsk, November 10–12, 2021. Krasnoyarsk: Siberian State University of Science and Technology; 2021. P. 685–687 (in Russ.). https://elibrary.ru/yjchna

7. Nosova S.A., Turlapov V.E. Detection of brain cells in optical microscopy based on textural features with machine learning methods. Program. Comput. Soft. 2019;45(4):171–179. https://doi.org/10.1134/S0361768819040054 [Original Russian Text: Nosova S.A., Turlapov V.E. Detection of brain cells in optical microscopy based on textural features with machine learning methods. Programmirovanie. 2019;4:36–45 (in Russ.). https://doi.org/10.1134/S0132347419040058 ]

8. Hamad Y.A., Zotin A.G., Simonov K.V., Medievsky A.V., Chizhova I.G. Detection and evaluation of breast pathology based on fuzzy clustering and discrete wavelet transform. Meditsina i vysokie tekhnologii = Medicine and High Technology. 2023;2:5–13 (in Russ.). https://www.elibrary.ru/ujqlwd

9. Raja R., Ganeshkumar P. QOSTRP: a trusted clustering based routing protocol for mobile ad-hoc networks. Program. Comput. Soft. 2018;44(6):407–416. https://doi.org/10.1134/S0361768818060099 [Original Russian Text: Raja R., Ganeshkumar P. QOSTRP: a trusted clustering based routing protocol for mobile ad-hoc networks. Programmirovanie. 2018;6:28–41 (in Russ.). https://doi.org/10.31857/S013234740002763-4 ]

10. Kasimov D.R., Kuchuganov A.V., Kuchuganov V.N., Oskolkov P.P. Approximation of color images based on the clusterization of the color palette and smoothing boundaries by splines and arcs. Program. Comput. Soft. 2018;44(5):295–302. https://doi.org/10.1134/S0361768818050043 [Original Russian Text: Kasimov D.R., Kuchuganov A.V., Kuchuganov V.N., Oskolkov P.P. Approximation of color images based on the clusterization of the color palette and smoothing boundaries by splines and arcs. Programmirovanie. 2018;5:3–11 (in Russ.). https://doi.org/10.31857/S013234740001211-7 ]

11. Bereshpolov I.S., Kravchenko Yu.A., Sleptsov A.G. Data clustering algorithm for protecting confidential information on the internet. Izvestiya YuFU. Tekhnicheskie nauki = Izvestiya SFedU. Engineering Sciences. 2023;3(233):74–85 (in Russ.). https://doi.org/10.18522/2311-3103-2023-3-74-85

12. Kharakhinov V.A., Sosinskaya S.S. The visualization methods for cluster analysis results of mechanical engineering components based on neural network. Programmnaya inzheneriya = Software Engineering. 2017;8(4):170–176 (in Russ.). https://doi.org/10.17587/prin.8.170-176

13. Kuchuganov V.N., Kuchuganov A.V., Kasimov D.R. Clustering algorithm for a set of machine parts on the basis of engineering drawings. Program. Comput. Soft. 2020;46(1):25–34. https://doi.org/10.1134/S0361768820010041 [Original Russian Text: Kuchuganov V.N., Kuchuganov A.V., Kasimov D.R. Clustering algorithm for a set of machine parts on the basis of engineering drawings. Programmirovanie. 2020;46(1):29–38 (in Russ.). https://doi.org/10.31857/S0132347420010045 ]

14. Matveeva I.Yu. Clustering of retail investors and portfolio structure by asset class. Izvestiya Sankt-Peterburgskogo gosudarstvennogo ekonomicheskogo universiteta. 2023;4(142):180–184 (in Russ.). https://www.elibrary.ru/fnmsql

15. Batrasova A.D., Konovalova T.V., Komarov P.I. Clustering as a method of studying the financial stability of IT companies. Upravlencheskii uchet = Management Accounting. 2022;(1-2):177–182 (in Russ.). https://www.elibrary.ru/xaufce

16. Anfyorov M.A. Formalization of the structural solutions search for CAD/CAM System. In: Computer Science: Problems, Methods, Technologies: Proceedings of the 22nd International Scientific and Practical Conference. Voronezh, Voronezh State University, February 10–12, 2022. Voronezh: VELBORN; 2022. P. 881–886 (in Russ.). https://elibrary.ru/dsdxhp

17. Yakovlev D.D., Petrov D.Yu. Application of exploratory data analysis for clusterization of robotic assembly complexes structures. Avtomatizirovannoe proektirovanie v mashinostroenii. 2024;17:71–75 (in Russ.). https://doi.org/10.26160/2309-8864-2024-17-71-75

18. Stepanov M.A. Method to identify structural transformations of time series using fuzzy clustering principles. Vestnik Ryazanskogo gosudarstvennogo radiotekhnicheskogo universiteta = Vestnik of Ryazan state Radioengineering University. 2019;69:149–159 (in Russ.). https://doi.org/10.21667/1995-4565-2019-69-149-159

19. Kladov D.E., Berikov V.B., Klimontov V.V. Time Series Clustering Algorithm and Its Application for Glycemic Curve Analysis. In: KNOWLEDGE – ONTOLOGY – THEORY: Proceedings of the 9th International Conference. Novosibirsk, October 2–6, 2023. Novosibirsk: Sobolev Institute of Mathematics, SB RAS; 2023. P. 154–161 (in Russ.). https://elibrary.ru/qqzzvw

20. Tishhenko A.K., Pliss I.P. Segmentation of Multidimensional Nonstationary Time Series Using the Fuzzy Clustering Method. Vostochno-Evropeiskii Zhurnal Peredovykh Tekhnologii. 2012;4(58):24–26 (in Russ.). Available from URL: https://cyberleninka.ru/article/n/segmentatsiya-mnogomernyh-nestatsionarnyh-vremennyh-ryadov-s-pomoschyu-metoda-nechetkoy-klasterizatsii. Accessed February 02, 2025.

21. Dulya I.S. Applying deep learning techniques to the time series clustering problem. Alleya nauki. 2021;1(5):974–978 (in Russ.). https://elibrary.ru/nokchy

22. Hurley C., Mclean J. Wavelet Analysis and Methods. Waltham Abbey: ED-Tech Press; 2021, 366 p.

23. Spirina P.V., Semenova A.R. Cluster analysis of the dynamics of innovation activities of the constituent entities of the Russian Federation. Ehkonomicheskie issledovaniya i razrabotki = Economic Development Research Journal. 2021;8:42–53 (in Russ.). https://elibrary.ru/oiqybo

24. Zhikhalkina N.F. Dynamic approach to clustering problem. Matematicheskie struktury i modelirovanie = Mathematical Structures and Modeling. 2000;5:133–139 (in Russ.). Available from URL: https://cyberleninka.ru/article/n/dinamicheskiy-podhod-k-zadache-klasterizatsii. Accessed February 02, 2025.

25. Ryzhkov D.V. About Dynamic Time Series Clustering Methods. In: Science. Technologies. Innovations: Collection of Scientific Papers of the 17th All-Russian Scientific Conference of Young Scientists. Novosibirsk, December 04–08, 2023. Novosibirsk: NSTU; 2024. P. 178–182 (in Russ.). https://elibrary.ru/xykvdt

26. Zaitsev R.D. Study of the Effectiveness of Multivariate Clustering of Time Series for the Analysis of the Dynamics of Scientific and Technological Development. Perspektivy razvitiya informatsionnykh tekhnologii. 2015;25:7–13 (in Russ.). https://www.elibrary.ru/uhrwjt

27. Ten Holt G.A., Reinders M.J.T., Hendriks E.A. Multi-dimensional dynamic time warping for gesture recognition. In: Thirteenth Annual Conference on the Advanced School for Computing and Imaging. Netherlands: V. 300. 2007, 8 p. Available from URL: https://www.researchgate.net/publication/228740947. Accessed February 02, 2025.

28. Kocheturov A.A., Batsyn M.V., Pardalos P.M. Dynamics of cluster structures in stock market networks. Zhurnal novoi ehkonomicheskoi assotsiatsii = The Journal of the New Economic Association. 2015;4(28):12–30 (in Russ.). https://www. elibrary.ru/vdzrqh

29. Kohonen T. Self-Organizing Maps. 3rd ed. Berlin – New York: Springer-Verlag; 2001, 521 p.

30. Anfyorov M.A. Clustering in decision-making. Informatsionnye tekhnologii. Problemy i resheniya. 2020;2(11):97–102 (in Russ.). https://www.elibrary.ru/rwddvo

31. Davydova G.V., Belikov A.Yu. Methodology for Quantitative Assessment of Enterprise Bankruptcy Risk. Upravlenie riskom. 1999;3:13–20 (in Russ.). https://www.elibrary.ru/tdgdrb


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A methodology for analyzing the functioning of socioeconomic systems was developed for increasing the efficiency of decisions made to manage these systems based on the clustering of temporal multidimensional data.

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Anfyorov M.A. Clustering of multidimensional temporal data as part of information support for management decisions. Russian Technological Journal. 2026;14(2):7-16. https://doi.org/10.32362/2500-316X-2026-14-2-7-16. EDN: VCTFHE

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ISSN 2782-3210 (Print)
ISSN 2500-316X (Online)