Data mining (DM) includes techniques for finding meaningful information hidden in these massive data stacks. The aim of this study is to divide the countries according to their prosperity levels with Cluster Analysis (CA), which is one of the DM techniques. In this context, the 2019 data of 167 countries within the updated 12 prosperity indicators in The Legatum Prosperity Index (LPI) were used. Countries were divided into clusters with the Ward’s algorithm, and the Elbow method was used for verifying of the optimal cluster number. The similarities between the countries were determined with the K-Means, and Tukiye's place in the clusters was determined. The results show that countries are divided into three clusters. The most significant indicators in separating them into clusters are "market access and infrastructure, education, investment environment", and the least significant indicators are "social capital, natural environment, safety and security". It has been determined that Turkiye is located in the middle prosperity level cluster and its "health, living conditions, education" indicators are the highest, while its "natural environment, personal freedom, management" indicators are the lowest.
Abu Sharkh, M. & Gough, I. (2010). Global welfare regimes a cluster analysis. Global Social Policy, 10(1), 27-58. DOI: 10.1177/1468018109355035
Akar, S. (2014). Türkiye’de daha iyi yaşam endeksi: OECD ülkeleri ile karşılaştırma [The better life index in Turkey: Comparison with OECD countries]. Journal of Life Economics, 1, 1-12. DOI: 10.15637/jlecon.201416987
Akar, H. (2015). Farklılaşan refah ölçüm yöntemleri ve eğitim açısından Türkiye’nin değerlendirilmesi [The diversifying measures of welfare and evaluation of Turkey in the context of education]. Finance, Politics & Economic Reviews, 52(606), 21-38. Retrieved from http://www.ekonomikyorumlar.com.tr/files/articles/152820006117_2.pdf
Akkuş, B. & Zontul, M. (2019). Veri madenciliği yöntemleri ile ülkeleri gelişmişlik ölçütlerine göre kümeleme üzerine bir uygulama [An application on clustering countries with data mining methods based on development criteria]. AURUM Journal of Engineering Systems and Architecture, 3(1), 51-64. Retrieved from https://dergipark.org.tr/tr/pub/ajesa/issue/47400/598179
Albayrak, A. S. & Koltan Yılmaz, Ş. (2009). Veri madenciliği: Karar ağacı algoritmaları ve İMKB verileri üzerine bir uygulama [Data mining: Decision tree algorithms and an application on ISE data]. Süleyman Demirel University Journal of Faculty of Economics Administ. Sciences, 14(1), 31-52. Retrieved from https://dergipark.org.tr/tr/pub/sduiibfd/issue/20831/223135
Ali, H. H. & Kadhum, L. E. (2017). K-Means clustering algorithm applications in data mining and pattern recognition. International Journal of Science and Research (IJSR), 6(8), 1577-1584.
Alptekin, N. & Yeşilaydın, G. (2015). OECD ülkelerinin sağlık göstergelerine göre bulanık kümeleme analizi ile sınıflandırılması [Dividing OECD countries according to health indicators using fuzzy clustering analysis]. Journal of Business Research Turk, 7(4), 137-155. Retrieved from https://isarder.org/index.php/isarder/article/view/274
Bambra, C. (2007). Defamilisation and welfare state regimes: A cluster analysis. International Journal of Social Welfare, 16, 326-338. DOI: 10.1111/j.1468-2397.2007.00486.x
Better Life Index (BLI) (2019). “What’s the Better Life Index?”, http://www.oecdbetterlifeindex.org/ about/better-life-initiative/, (Accessed Date: 28.12. 2020).
Bholowalia, P. & Kumar, A. (2014). EBK-Means: A Clustering technique based on Elbow method and K-Means in WSN. International Journal of Computer Applications, 105(9), 17-24. DOI: 10.5120/18405-9674
Budsaratragoon, P. & Jitmaneeroj, B. (2021). Reform priorities for prosperity of nations: The Legatum Index. Journal of Policy Modeling, 43, 657-672. DOI: 10.1016/j.jpolmod.2020.09.004
Büchs, M. (2021). Sustainable welfare: Independence between growth and welfare has to go both ways. Global Social Policy, 21(2), 323-327. DOI: 10.1177/14680181211019153
Chatzopoulos, D. & Derri, V. (2004). Grading profiles of high school physical educators: A cluster analysis. Journal of Human Movement Studies, 47, 061-073.
Crowther, D., Kim, S., Lee, J., Lim, J. & Loewen, S. (2021). Methodological synthesis of cluster analysis in second language research. Language Learning, 71(1) 99-130. DOI: 10.1111/lang.12428
Demiralay, M. & Çamurcu, A. Y. (2005). CURE, AGNES ve K-Means algoritmalarındaki kümeleme yeteneklerinin karşılaştırılması [Comparison of clustering characteristics of CURE, AGNES and K-Means algorithms]. İstanbul Commerce University Journal of Science, 4(8), 1-18. Retrieved from https://dergipark.org.tr/tr/pub/ticaretfbd/issue/21348/229000
Değirmenci, N. & Yakıcı Ayan, T. (2020). OECD ülkelerinin sağlık göstergeleri açısından bulanık kümeleme analizi ve TOPSIS yöntemine göre değerlendirilmesi [Evaluation of OECD countries according to fuzzy clustering analysis and TOPSIS method in terms of health indicators]. Hacettepe University Journal of Economics and Administrative Sciences, (38)2, 229-241. DOI: 10.17065/huniibf.592991
Dinç Cavlak, Ö. (2019). Sürdürülebilir toplum göstergelerinin hiyerarşik kümeleme analizi yöntemiyle incelenmesi [Hierarchical clustering analysis of sustainable society indicators]. Third Sector Social Economic Review, 54(4), 2053-2073. DOI: 10.15659/3.sektor-sosyal-ekonomi.19.12.1125
Egloff, B., Schmukle, S. C., Burns, L. R., Kohlmann, C. W. & Hock, M. (2003). Facets of dynamic positive affect: Differentiating joy, interest, and activation in the positive and negative affect schedule (PANAS). Journal of Personality and Social Psychology, 85(3), 528-540. DOI: 10.1037/0022-3514.85.3.528
Global Competitiveness Index (GCI) (2019). “Global Competitiveness Report”, https://www.weforum.org/ reports/how-to-end-a-decade-of-lost-productivity-growth, (Accessed Date: 11. 01. 2021).
Gülden, T. & Karakış, E. (2019). OECD ülkelerinin ekonomik özgürlüklerine göre kümeleme analizi ile sınıflandırılması [Classification of OECD countries according to economic freedom with cluster analysis]. Journal of Economics and Administrative Sciences, 20(2), 297-316. Retrieved from http://esjournal.cumhuriyet.edu.tr/tr/pub/issue/50375/614708
Han, J., Kamber, M. & Pei, J. (2012). Data mining concepts and techniques. Waltham: Morgan Kaufmann Publishers is An Imprint of Elsevier.
Humaira, H. & Rasyidah, R. (2018). Determining the appropiate cluster number using Elbow method for K-Means algorithm, WMA-2 2018, DOI: 10.4108/eai.24-1-2018.2292388
Jeon, J. Y., Choi, J. S. & Byun, H. G. (2016). Implementation of Elbow method to improve the gases classification performance based on the RBFN-NSG Algorithm. Journal of Sensor Science and Technology, 25(6), 431-434. DOI: 10.5369/JSST.2016.25.6.431
Kangallı, S. G., Uyar, U. & Buyrukoğlu, S. (2014). OECD ülkelerinde ekonomik özgürlük: bir kümeleme analizi [Economic freedom in OECD countries: A cluster analysis]. International Journal of Alanya Faculty of Business, 6(3), 95-109. Retrieved from https://dergipark.org.tr/tr/pub/uaifd/issue/21601/231994
Ketchen, D. J. Jr. & Shook, C. L. (1996). The application of cluster analysis in strategic management research: an analysis and critique. Strategic Management Journal, 17, 441-458. Retrieved from http://www.jstor.org/stable/2486927
Koltan Yılmaz, Ş. & Patır, S. (2011). Kümeleme analizi ve pazarlamada kullanımı [Cluster analysis and its usage in marketing]. Journal of Academic Approaches, 2(1), 91-113. Retrieved from https://dergipark.org.tr/tr/pub/ayd/issue/3325/46150
Kowalski, R. & Wałęga, G. (2015). Defamilisation in Central and Eastern Europe: A Cluster Analysis. The 9th International Days of Statistics and Economics, September 10-12, Prague, 855-863.
Levent, M. & Özarı, Ç. (2019). EDAS yöntemi ve kümeleme analizi ile G-10 ülkelerinin ekonomik özgürlük kriterleri ile değerlendirilmesi [Evaluating economic freedom’ criterias of G-10 countries with EDAS method and cluster analysis]. The Journal of Turk-Islam World Social Studies, 6(22), 219-235. DOI: 10.29228/TIDSAD.30876
Levy-Carciente, S., Phélan, C. M. & Perdomo, J. (2020). Prosperity in Spain and Latin America: myths and facts. International Journal of Advance Study and Research Work, 3(7), 2581-5997. DOI: 10.5281/zenodo.3958006
Mamat, A. R., Mohamed, F. S., Mohamed, M. A., Rawi, N. M. & Awang, M. I. (2018). Silhouette index for determining optimal k-means clustering on images in different color models. International Journal of Engineering & Technology, 7(2.14), 105-109. DOI: 10.14419/ijet.v7i2.14.11464
Markou, G., Palaiolouga, E., Kokkinakos, P., Markaki, O., Koussouris, S. & Askounis, D. (2015). “Prosperity Indicators: A Landscape Analysis”, http://ceur-ws.org/Vol-1553/paper6.pdf, (Accessed Date: 08.02.2021).
Maylawati, D.S., Priatna,T. Sugilar, H. & Ramdhani, M.A. (2020). Data science for digital culture improvement in higher education using K-means clustering and text analytics. International Journal of Electrical and Computer Engineering (IJECE), 10(5), 4569-4580. DOI: 10.11591/ijece.v10i5.pp4569-4580
Morissette, L. & Chartier, S. (2013). The K-Means clustering technique: General considerations and implementation in Mathematica, Tutorials in Quantitative Methods for Psychology, 9(1), 15-24. DOI:10.20982/tqmp.09.1.p015
Murtagh, F. & Legendre, P. (2014). Ward’s hierarchical agglomerative clustering method: Which algorithms implement Ward’s criterion?. Journal of Classification, 31, 274-295. DOI: 10.1007/s00357-014-9161-z
Mut, S. & Akyürek, Ç. E. (2017). OECD ülkelerinin sağlık göstergelerine göre kümeleme analizi ile sınıflandırılması [Classifying OECD countries according to health indicators using clustering analysis]. International Journal of Academic Value Studies (Javstudies), 3(12), 411-422. DOI: 10.23929/javs.283
Nidheesh, N., Abdul Nazeer, K. A. & Ameer, P. M. (2020). A Hierarchical Clustering algorithm based on Silhouette Index for cancer subtype discovery from genomic data. Neural Computing and Applications, 32, 11459-11476. DOI: 10.1007/s00521-019-04636-5
Ogbuabor, G. & Ugwoke, F. N. (2018). Clustering algorithm for a healthcare dataset using Silhouette score value. International Journal of Computer Science & Information Technology (IJCSIT), 10(2), 27-37. DOI: 10.5121/ijcsit.2018.10203
Özdamar, K. (2004). Paket Programlar İle İstatistiksel Veri Analizi 2 [Statistical Data Analysis with Package Programs 2]. Eskişehir, Turkey: Kaan Kitabevi.
Peiro-Palomino J. & Picazo-Tadeo, A.J (2018). OECD: one or many? Ranking countries with a composite well-being indicator. Soc Indic Res, 139, 847-869. DOI: 10.1007/s11205-017-1747-5
Saputra, D. M., Saputra, D. & Oswari, L. D. (2019). Effect of distance metrics in determining k-value in Kmeans clustering using Elbow and Silhouette method. Advances in Intelligent Systems Research, 172, 341-346. DOI: 10.2991/aisr.k.200424.051
Shahapure, K. R. & Nicholas, C. (2020). “Cluster quality analysis using Silhouette score", 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 747-748.
Shahbaz, M., Iftikhar, M. & Mahmood, R. (2013). Classification based on Empathy level by Mining Economic Prosperity and Environmental Indicators. International Journal for e-Learning Security (IJeLS), 3(2), 340-349. DOI: 10.20533/ijels.2046.4568.2013.0043
Shi, C., Wei, B., Wei, S., Wang, W., Liu, H. & Liu, J. (2021). A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm. J Wireless Com Network, 31. DOI: 10.1186/s13638-021-01910-w
Söküt Açar, T. & Ayman Öz, N. (2020). The determination of optimal cluster number by Silhouette index at clustering of the European Union member countries and candidate Turkey by waste indicators. Pamukkale Univ Muh Bilim Derg, 26(3), 481-487. DOI: 10.5505/pajes.2019.49932
Syakur, M. A., Khotimah, B. K., Rochman, E. M. S. & Satoto, B. D. (2018). Integration K-Means clustering method and Elbow method for identification of the best customer profile, IOP Conf. Series: Materials Science and Engineering, 336, 012017.
Taşçı, M. & Özarı, Ç. (2019), OECD ülkelerinin ekonomik özgürlük göstergelerinin K-Ortalamalar kümeleme yöntemi ve Gri İlişkisel Yöntemi ile analizi [Evaluating economic freedom criterias of OECD countries with grey relational analysis method and cluster analysis]. The Journal of Academic Social Science, 7(96), 464-488. DOI : 10.29228/ASOS.36738
Timor, M. & Yüzbaşı Künç, G. (2021). Ekonomik gelişmişliği etkileyen bilgi ekonomisi değişkenlerinin veri madenciliği ile belirlenmesi [Determination of knowledge economy variables that affect economic development by using data mining]. Optimum Journal of Economics and Management Sciences, 8(1), 1-18. DOI: 10.17541/optimum.748237
Turan, K. K., Özarı, Ç. & Demir, E. (2016). Kümeleme analizi ile Türkiye ve Ortadoğu ülkelerinin ekonomik göstergeler açısından karşılaştırılması [Comparing Turkey and The Middle East countries with cluster analysis: Economic perspective]. Istanbul Aydin University Journal, 29, 143-165. DOI: 10.17932/IAU.IAUD.m.13091352.2016.8/29.143-165
Tüzüntürk, S. (2010). Veri madenciliği ve istatistik [Data mining and statistics]. Bursa Uludağ Journal of Economy and Society, 29(1), 65-90.
Umargono, E., Suseno, J. & Vincensius Gunawan, S. K. (2019).K-Means Clustering Optimization using the Elbow Method and Early Centroid Determination Based-on Mean and Median, In Proceedings of the International Conferences on Information System and Technology (CONRIST 2019), 234-240.
United Nations Development Program (UNDP) Human Development Index (HDI) (2019). “About Human Development”, http://hdr.undp.org/en/humandev, (Accessed Date: 13. 07. 2020).
Wulandari, S. (2020). Analyze k-value selected method of k-means clustering algorithm to clustering province based on disease case. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(3), 121-124.
Yuan, C. & Yang, H. (2019). Research on K-Value Selection Method of K-Means Clustering Algorithm. J, 2(2), 226-235. DOI: 10.3390/j2020016
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
scan QR code to access this article from your mobile device
Contact Us
Faculty of Transportation and Logistics, Istanbul University Beyazit Campus 34452 Fatih/Istanbul/TURKEY
alphanumeric journal has been publishing as "International Peer-Reviewed Journal" every six months since 2013. alphanumeric serves as a vehicle for researchers and practitioners in the field of quantitative methods, and is enabling a process of sharing in all fields related to the operations research, statistics, econometrics and management informations systems in order to enhance the quality on a globe scale.