Improving the SOM algorithm to ensure stability and reproducibility of data clustering results
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2026.1.08Keywords:
Kohonen self-organizing maps (SOM), data clustering, seed parameter, reproducibility of results, random number generatorAbstract
The article proposes a method to improve the Kohonen Self-Organizing Map (SOM) learning algorithm to ensure the stability and reproducibility of clustering results, an urgent task when working with large amounts of data. SOM is widely used in clustering and visualization tasks, especially in applications that require analyzing multidimensional data structures, such as telecommunications billing systems and financial analysis. The standard SOM implementation, which includes random weight initialization and stochastic sample selection during training, leads to significant cluster variability even when using the same input data and identical network training parameters. This makes it difficult to apply this algorithm in cases where stability and reproducibility of results are required. To solve this problem, we propose modifying the algorithm to include its own random number generator and introducing a seed parameter to fix the initial training conditions. This reduces variability and ensures reproducible clustering results, thereby increasing the reliability of the analysis and the suitability of the SOM algorithm for real business tasks. The proposed method has been tested on data from billing systems, where the reproducibility of clustering results is critical for effective work with customer segments, the development of targeted marketing strategies, and the creation of personalized tariff plans.References
“Teuvo Kohonen, Timo Honkela, Kohonen Network,” Scholarpedia. 2007. Accessed on: 12 June 2024. Available: http://www.scholarpedia.org/article/Self-organizing_feature_map
Panu Somervuo, Teuvo Kohonen, “Clustering and Visualization of Large Protein Se-quence Databases by Means of an Extension of the Self-Organizing Map,” Lecture Notes in Computer Science, vol. 1967, 2000. doi: https://doi.org/10.1007/3-540-44418-1_7
Marc M. Van Hulle, “Self-Organizing Maps,” Handbook of Natural Computing. Springer, Berlin, Heidelberg, pp. 585–622, 2012.
Jens Christian Claussen, “Winner-Relaxing Self-Organizing Maps,” Neural Computa-tion, 17(5), pp. 996–1009, 2005. doi: https://doi.org/10.1162/0899766053491922
Melody Y. Kiang, Michael Y. Hu, Dorothy M. Fisher, “An extended self-organizing map network for market segmentation — a telecommunication example,” Decision Sup-port Systems, vol. 42, issue 1, October 2006, pp. 36–47. doi: https://doi.org/10.1016/j.dss.2004.09.012
W. Wang, S. Xu, H. Ouyang, X. Zeng, “Parameter Optimization of the Power and En-ergy System of Unmanned Electric Drive Chassis Based on Improved Genetic Algo-rithms of the KOHONEN Network,” World Electric Vehicle Journal, 14(9), 260, 2023. doi: https://doi.org/10.3390/wevj14090260
V. Diachenko, O. Liashenko, B.F. Ibrahim, O. Mikhal, Yu. Koltun, “Kohonen network with parallel training: Operation structure and algorithm,” Int. J. Adv. Trends Comp. Sci. Eng., vol. 8, no. 1.2, pp. 35–38, 2019. doi: https://doi.org/10.30534/ijatcse/2019/0681.22019
Rodrigo B. de C. Cavalcanti, Bruno Pimentel, Carlos W.D. de Almeida, Renata M.C.R. de Souza, “A Multivariate Fuzzy Kohonen Clustering Network,” IEEE Trans-actions on Neural Networks, vol. 31, no. 4. pp. 75–82, 2020. doi: https://doi.org/10.1109/IJCNN.2019.8852243
N.I. Furmanova, O.Y. Farafonov, O.Y. Malyi, Y.O. Sitsilitsyn, “Improvement of the method of searching for solutions to solve the optimization problem using a genetic al-gorithm by preliminary clustering,” (in Ukrainian), Instrumentation Technology, no. 2, pp. 6–9, 2017. Available: https://elar.tsatu.edu.ua/server/api/core/bitstreams/f045c4ca-7d17-4c9c-a1cb-7b2df9aafe7e/content
V.O. Dyachenko, O.F. Mikhal, “Prospects for the use of of the classical Kohonen algo-rithm in distributed energy-critical sensor networks,” (in Ukrainian), Control, Naviga-tion and Communication Systems, no. 4, pp. 75–79, 2023. doi: https://doi.org/10.26906/SUNZ.2023.4.075
E.A. Egorova, V.G. Ivanov, E.S. Sakalo, “Optimization of process of the Kohonen self-organizing map based on the Kalman-Mayne filter,” (in Russian), Control, Navigation and Communication Systems, issue 4(8), pp. 52–55, 2008. Available: https://dspace.nlu.edu.ua/bitstream/123456789/6713/1/Ivanov_52-55.pdf
Achraf Khazri, “Self-Organizing Maps (Kohonen’s maps),” Medium, [website]. 2019. Available: https://medium.com/data-science/self-organizing-maps-1b7d2a84e065
T. Kohonen, “The self-organizing map,” Proceedings of the IEEE, 78(9), pp. 1464–1480, 1990.
E.O. Kaminsky, Forecasting system for supporting activities using deep learning. Bachelor’s thesis. NTUU “KPI”, Kyiv, 2023, p. 78.
Kohonen Networks, [website]. Accessed on: 13.11.2022. Available: https://ppt-online.org/46514
T. Kohonen, Self-Organizing Maps. Springer-Verlag Berlin Heidelberg, 2001.
S. Kaski, “Self-Organizing Maps,” in C. Sammut, G.I. Webb (Eds.) Encyclopedia of Machine Learning. Springer, Boston, 2011, pp. 886–888.
“Self-Organizing Maps, SOM,” Studfile.net. Accessed on: 15 Nov. 2022. Available: https://studfile.net/preview/3021431/
