K-ALLY BASED DYNAMIC FUZZY CLUSTERING FOR GEOPOLITICAL ALLIANCE ANALYSIS: A CASE STUDY INSPIRED BY THE RUSSIAN-UKRAINIAN CONFLICT

Geopolitical alliances are often based on a combination of factors such as geographic proximity, military strength, and strategic interests. In this research, we introduce the K-Ally algorithm based on Dynamic Fuzzy Clustering to dynamically analyze alliance patterns between countries. Using fuzzy logic and adaptive thresholds, this algorithm evaluates the potential benefits of alliances based on key attributes, such as geographic distance and power differences. This study is inspired by the allied dynamics that emerged in the Russian-Ukrainian war, where changes in strategy and international relations were key to the continuation of the conflict. The paper also compare this algorithm with the K-Means method commonly used in geopolitical data analysis. Experimental results show that K-Ally based on Dynamic Fuzzy Clustering is able to capture alliance dynamics better than K-Means, especially in conditions of uncertainty or attribute imbalance between countries. This research contributes to the development of new analytical tools for the study of geopolitics and international conflict.