Удосконалення алгоритму SOM для забезпечення стабільності та відтворюваності результатів кластеризації даних

• Date: 2026
• Main Authors: Ivashchenko, Oleksandr, Fedin, Serhii
• Format: Article
• Language: English
• Published: The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2026
• Subjects: самоорганізаційні карти Кохонена (SOM); кластеризація даних; параметр seed; відтворюваність результатів; генератор випадкових чисел
• Online Access: https://journal.iasa.kpi.ua/article/view/358080
• Journal Title: System research and information technologies

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.