This article presents the theoretical foundations and the learning algorithm of self-organizing Kohonen maps through mathematical models. Kohonen maps are considered an effective neural network model for unsupervised clustering and visualization of data. The paper provides a detailed description of the method's architecture, the process of finding the Best Matching Unit (BMU), the neighborhood model, and the adaptive weight updating process. Additionally, visualization techniques – the Unified Distance Matrix (U-Matrix) and component projections – are discussed. Practical applications of the method in medicine, geospatial data, and agriculture are analyzed, highlighting its importance as a tool for understanding data structures and supporting decision-making.
| Mualliflar | Қудайбергенов, А.А., Бекмуродова, Р.С. |
|---|---|
| Jurnal | Ilim ha’m ja’miyet |
| Nashr sanasi | 2025-06-27 |
| Son | 4-1 |
| Betlar | 20-22 |
| Til | O‘zbek |
Кохонен хариталари, нейрон тармоқ, кластерлаш, назоратсиз ўрганиш, визуализация, маълумот таҳлили, ўлчам пасайтириш, карта Кохонена, нейронная сеть, кластеризация, обучение без учителя, визуализация, анализ данных, понижение размерности, Kohonen map, neural network, clustering, unsupervised learning, visualization, data analysis, dimensionality reduction
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