<![CDATA[The G20 countries are often treated as a relatively homogeneous group in macroeconomic analysis, despitesubstantial differences in income levels, economic growth, and consumption patterns among member countries.This study aims to classify G20 countries based on national income, economic growth, and consumptionindicators in order to identify structural differences in their economic characteristics. The analysis employsthe K-Means clustering method using standardized data to ensure comparability across variables with differentmeasurement scales. Prior to clustering, data standardization is applied using the Z-score method. The optimalnumber of clusters is determined using a cluster validity measure, and the clustering process is performedusing Euclidean distance. The results indicate that the optimal clustering structure is achieved with threeclusters. The K-Means algorithm successfully groups G20 countries into three distinct clusters with clearlydifferentiated economic profiles. The centroid analysis reveals that each cluster exhibits unique characteristicsin terms of income level, growth dynamics, and consumption patterns, allowing for objective and data-drivencluster categorization. The findings also show that higher income levels are not always associated with higherconsumption patterns, and that developing economies tend to form a separate cluster characterized byrelatively higher economic growth. The evaluation of cluster quality indicates good cohesion within clustersand clear separation between clusters, suggesting that the clustering results are valid and reliable. Overall,this study demonstrates that cluster analysis provides an effective framework for capturing the heterogeneityof economic structures among G20 countries. The findings contribute to a more nuanced understanding ofglobal economic diversity and may serve as a basis for comparative economic analysis and policy orienteddiscussions.]]>
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