Clustering countries according to the world happiness report 2019

Main Article Content

M. Mujiya Ulkhaq
Arga Adyatama

Abstract

Since the first initiative in 2012, the World Happiness Report (WHR) has drawn international attention as it can help the policymakers to evaluate their policy options. In the 2019 edition, the WHR introduced six factors to describe the variation of happiness across the countries. Finland is declared as the happiest country just like what they did in the previous year. This study tried to cluster the countries according to the WHR 2019. Nine clustering algorithms were presented, and three internal validation indices were utilized to compare the algorithms. k-medoids algorithm was selected to illustrate how three distinguished clusters generated from the algorithm are different from each other. This study is expected to give an insight into how to implement clustering algorithms into the real-world data set and how to interpret the results.

Article Details

How to Cite
Ulkhaq, M. M., & Adyatama, A. (2021). Clustering countries according to the world happiness report 2019. Engineering and Applied Science Research, 48(2), 137–150. Retrieved from https://ph01.tci-thaijo.org/index.php/easr/article/view/240569
Section
ORIGINAL RESEARCH

References

Lyubomirsky S. The how of happiness: a scientific approach to getting the life you want. New York: Penguin Press; 2008.

Cantril H. The pattern of human concern. New Brunswick: Rutdgers University Press; 1965.

Helliwell JF, Layard R, Sachs J. World happiness report 2019. New York: Sustainable Development Solutions Network; 2019.

Kapourani CA, Sanguinetti G. Melissa: Bayesian clustering and imputation of single-cell methylomes. Genome Biol. 2019;20:61.

Utami AA, Ginanjar AR, Fadlia N, Lubis IA, Ulkhaq MM. Using shopping and time attitudes to cluster food shoppers: An empirical finding from Indonesia. J Phys: Conf Ser. 2019;1284:012005.

Minako FS, Ulkhaq MM, ‘Sa Nu D, Pratiwi ARA, Akshinta PY. Clustering internet shoppers: An empirical finding from Indonesia. Proceedings of the 2019 5th International Conference on E-business and Mobile Commerce; 2019 May 22-24; Taichung, Taiwan. Taiwan: Association for Computing Machinery; 2019. p. 35-9.

Ulkhaq MM, Fidiyanti F, Adyatama A, Maulani ZA, Nugroho AS. Segmentation of cinema audiences: an empirical finding from Indonesia. Proceedings of the 2019 2nd International Conference on Data Storage and Data Engineering; 2019 Jun 15-18; Jeju, Korea. Korea: Association for Computing Machinery; 2019. p. 3-8.

Brusco MJ, Steinley D, Stevens J, Cradit JD. Affinity propagation: an exemplar‐based tool for clustering in psychological research. Br J Math Stat Psychol. 2019;72:155-82.

John R, Ramesh H. Colour based segmentation of a landsat image using k-means clustering algorithm. J Image Process Pattern Recogn Progr. 2017;4(3):31-8.

Unglert K, Radić V, Jellinek AM. Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra. J Volcanol Geoth Res. 2016;320:58-74.

Helliwell JF, Layard R, Sachs J, De Neve JE. World happiness report 2020. New York: Sustainable Development Solutions Network; 2020.

The World Factbook [Internet]. 2020 [cited 2020 Apr]. Available from: https://www.cia.gov/library/publications/the-world-factbook/fields/208rank.html.

Salomon JA, Wang H, Freeman MK, Vos T, Flaxman AD, Lopez AD, et al. Healthy life expectancy for 187 countries, 1990–2010: a systematic analysis for the Global Burden Disease Study 2010. Lancet. 2012;380:2144-62.

The Worldwide Governance Indicators Project [Internet]. 2020 [cited 2020 Apr]. Available from: https://info.worldbank.org/governance/wgi/.

Manly BFJ, Alberto JAN. Multivariate statistical methods: a primer. 4th ed. Boca Raton: CRC Press; 2017.

Theodoridis S, Koutroumbas, K. Pattern recognition. 2nd ed. San Diego: Academic Press; 2008.

Pal NR, Biswas J. Cluster validation using graph theoretic concepts. Pattern Recogn. 1997;30(6):847-57.

Fraley C, Raftery AE. How many clusters? Which clustering method? answers via model-based cluster analysis. The Comput J. 1998;41(8):578-88.

Xu R, Wunsch II D. Survey of clustering algorithms. IEEE Trans Neural Network. 2005;16(3):645-78.

Mouton JP, Ferreira M, Helberg ASJ. A comparison of clustering algorithms for automatic modulation classification. Expert Syst Appl. 2020;151:113317.

Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM Comput Surv. 1999;31:264-323.

McQueen J. Some methods for classification and analysis of multivariate observations. In: Le Cam LM, Neyman J, editors. Berkeley Symposium on Mathematical Statistics and Probability; 1965 Dec 27 – 1966 Jan 7; Berkeley, USA. USA: The Regents of the University of California; 1967. p. 281-97.

Arthur D, Vassilvitskii S. k-means++: the advantages of careful seeding. Proceedings of the 18th annual ACM-SIAM symposium on Discrete algorithms; 2007 Jan 7-9; New Orleans, Louisiana. USA: Society for Industrial and Applied Mathematics; 2007. p. 1027-35.

Kaufman L, Rousseeuw PJ. Finding groups in data: an introduction to cluster analysis. Hoboken: John Wiley & Sons; 1990.

Kaufman L, Rousseeuw PJ. Clustering large data sets (with discussion). In: Gelsema ES, Kanal LN, editors. Pattern recognition in practice II. Amsterdam: Elsevier; 1986. p. 425-37.

Frey BJ, Dueck D. Clustering by passing messages between data points. Science. 2007;315:972-6.

Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. Canada: Springer; 2017.

Ester M, Kriegel HP, Sander J, Xu X. Density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. USA: AAAI; 1996. p. 226-31.

Halkidi M, Batistakis Y, Vazirgiannis M. On clustering validation techniques. J Intell Inform Syst. 2001;17: 107-45.

Arbelaitz O, Gurrutxaga I, Muguerza J, Pérez JM, Perona I. An extensive comparative study of cluster validity indices. Pattern Recogn. 2013;46(1):243-56.

Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1986;20:53-65.

Dunn JC. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybernetics. 1973;3(3):32-57.

Caliński T, Harabasz J. A dendrite method for cluster analysis. Commun Stat. 1974;3:1-27.

Theil H. Introduction to econometrics. Prentice Hall: Englewood Cliffs; 1978.

James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning with applications in R. 7th ed. New York: Springer; 2013.

Dimitriadou E, Dolñicar S, Weingessel A. An examination of indexes for determining the number of clusters in binary data sets. Psychometrika. 2002;67:137-59.

Maulik U, Bandyopadhyay S. Performance evaluation of some clustering algorithms and validity indices. IEEE Trans Pattern Anal Mach Intell. 2002;24(12):1650-4.

Milligan GW, Cooper MC. An examination of procedures for determining the number of clusters in a data set. Psychometrika. 1985;50:159-79.

Brun M, Sima C, Hua J, Lowey J, Carroll B, Suh E, et al. Model-based evaluation of clustering validation measures. Pattern Recogn. 2007;40(3):807-24.

United Nations. World economic situation and prospects 2020. New York: United Nations; 2020.

Cloutier S, Jambeck J, Scott N. The Sustainable Neighborhoods for Happiness Index (SNHI): a metric for assessing a community’s sustainability and potential influence on happiness. Ecol Indicat. 2014;40:147-52.

Cloutier S, Pfeiffer D. Sustainability through happiness: a framework for sustainable development. Sust Dev. 2015;23:317-27.

Carlsen L. Happiness as a sustainability factor. The world happiness index: a posetic-based data analysis. Sustain Sci. 2018;13:549-71.