Cluster Quality in Agriculture: Assessing GDP and Harvest Patterns in Asia and Europe with K-Means and Silhouette Scores

This study presents an analysis of global economies using K-Means clustering to classify countries based on their average Gross Domestic Product (GDP) and harvested area over multiple years (1980-2020). The objective was to identify distinct groupings that could inform economic and agricultural practices. The algorithm was set to two clusters, revealing a stark contrast between a large, diverse group of countries and a smaller cluster consisting exclusively of China and India. The first cluster included nations with a wide range of economic outputs and agricultural scales, whereas the second was characterized by significantly larger harvested areas. A scatter plot visualization demonstrated the clear separation between the clusters, emphasizing the unique economic and agricultural status of the two Asian giants. This clustering analysis not only highlights the disparities in global economic and agricultural profiles but also provides a quantitative basis for future strategic planning.