Clustering the Economic Conditions of Various Countries From 2010-2023 by Conducting A Comparative Analysis of the K-Means and K-Medoids Algorithms
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Yunita Yunita
*
STMIK Widya Cipta Dharma, Samarinda,
Indonesia
- Hanifah Ekawati STMIK Widya Cipta Dharma, Samarinda, Indonesia
- Amelia Yusnita STMIK Widya Cipta Dharma, Samarinda, Indonesia (*) Corresponding Author
Abstract
Understanding the similarities and differences in economic conditions across countries is crucial for various stakeholders. This research investigates the global economic landscape by clustering countries based on their economic indicators, including GDP, inflation rate, unemployment rate, and economic growth, spanning the period of 2010 to 2023. This timeframe encompasses significant global economic events, making it pertinent for analysis. The study employs and compares two prominent clustering algorithms: K-Means and K-Medoids, to identify groups of countries exhibiting similar economic patterns. Utilizing secondary data from Kaggle encompassing 19 countries, the research assesses the ability of each algorithm to delineate meaningful economic clusters. The K-Means algorithm, with a determined optimal number of four clusters, demonstrated a reasonably good cluster separation and moderate internal cohesion, evidenced by a Silhouette Coefficient of 0.58 and a Davies-Bouldin Index of 0.63. In contrast, the K-Medoids algorithm yielded a distinct clustering structure with a lower Silhouette Coefficient (0.26) and a higher Davies-Bouldin Index (1.16), suggesting less distinct cluster separation and potential sensitivity to data characteristics. This comparative analysis provides insights into the applicability and performance of K-Means and K-Medoids in discerning global economic structures, contributing to a deeper understanding of the world economic map and the utility of clustering techniques in economic data analysis.
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