Abstract:
This research focuses on clustering 167 countries based on welfare indicators using a non-hierarchical cluster analysis method, specifically the K-Medoids algorithm. Welfare, which includes economic, social, health, environmental, and security aspects, is the main goal of every country's development. In this study, welfare indicators such as health, exports, imports, and income are used as parameters for clustering. The K-Medoids method is used to cluster countries based on the similarity of welfare indicator characteristics. Validation of the cluster results was done using the Silhouette and Dunn indices. The results showed that there were two optimal clusters with a Silhouette index value of 0.2846391 and a Dunn index of 0.0666. This research is expected to provide new insights into how countries can be grouped based on welfare indicators and contribute to efforts to improve global welfare.
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