The World Happiness Report (WHR) has drawn international attention since the first initiative in 2012 as it can help the policy makers to evaluate their policy options. There are six factors to describe the variation of the happiness across the countries, i.e., gross domestic product per capita, social support, healthy life expectancy, freedom to make life choices, perception of corruption, and generosity. This study aims to cluster the countries according to the WHR 2020. Nine clustering algorithms (k-means, k-means++, k-medoids, clustering large applications, affinity propagation, spectral clustering, density-based spatial clustering of applications with noise, agglomerative nesting, and divisive analysis) are presented and three internal validation indices (silhouette index, Dunn’s index, and Calinski-Harabasz’ index) are utilized to compare the algorithms. This study is expected to give an insight about how to implement clustering algorithms into the real world (not artificial) data set and how to interpret the result.
Dipartimento di Economia e Management - Università di Brescia - Via San Faustino 74/B - 25122 BRESCIA (e-mail: firstname.lastname@example.org).