Analysis of The Countries According to The Prosperity Level with Data Mining

Year 2022, Volume: 10 Issue: 2, 85 - 104, 31.12.2022

Şebnem Koltan Yılmaz , Sibel Şener

https://doi.org/10.17093/alphanumeric.1002461

Cited By: 1

Abstract

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 into groups according to their prosperity levels with Cluster Analysis (CA), which is one of the DM techniques, and to show the applicability of the method. In this context, the 2019 data of 167 countries within the 12 prosperity indicators in The Legatum Prosperity Index (LPI) were used. In the study, countries were divided into groups with the Ward’s algorithm and the similarities between the countries were determined with the K-Means and Turkey's place in the groups was determined. The results show that countries are divided into three clusters according to their prosperity levels. The most effective indicators in dividing them into clusters are "market access and infrastructure, education, investment environment", and the least effective indicators are "social capital, natural environment, safety and security". It has been determined that Turkey 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.

Keywords

Data Mining, Prosperity Level, Legatum Prosperity Index, Cluster Analysis.

References

• Abu Sharkh, M. and Gough, I. (2010). Global welfare regimes a cluster analysis. Global Social Policy, 10(1), 27–58.
• Akar, S. (2014). Türkiye’de daha iyi yaşam endeksi: OECD ülkeleri ile karşilaştirma (The better life index in Turkey: Comparison with OECD countries). Journal of Life Economics, 1, 1-12.
• 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). Finans Politik & Ekonomik Yorumlar, 52(606), 21-38.
• Akkuş, B. and 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 Mühendislik Sistemleri ve Mimarlık Dergisi, 3(1), 51-64.
• Albayrak, A. S. and 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 Üniversitesi İktisadi ve İdari Bilimler Dergisi, 14(1), 31-52.
• Ali, H. H. and 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. and Yeşilaydın, G. (2015). OECD ülkelerinin sağlık göstergelerine göre bulanık kümeleme analizi ile sınıflandırılması (Classifying OECD countries according to health indicators using fuzzy clustering analysis). İşletme Araştırmaları Dergisi, 7(4), 137-155.
• Bambra, C. (2007). Defamilisation and welfare state regimes: A cluster analysis. International Journal of Social Welfare, 16, 326–338.
• BLI, Better Life Index (2019). “What’s the Better Life Index?”, http://www.oecdbetterlifeindex.org/about/better-life-initiative/, (Accessed Date: 28.12. 2020).
• Budsaratragoon, P. and Jitmaneeroj, B. (2021). Reform priorities for prosperity of nations: The Legatum Index. Journal of Policy Modeling, 43, 657–672.
• Büchs, M. (2021). Sustainable welfare: Independence between growth and welfare has to go both ways. Global Social Policy, 21(2), 323-327.
• Demiralay, M. and 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 Ticaret Üniversitesi Fen Bilimleri Dergisi, 4(8), 1-18.
• Değirmenci, N. and 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 Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (38)2, 229-241.
• Dinç Cavlak, Ö. (2019). Sürdürülebilir toplum göstergelerinin hiyerarşik kümeleme analizi yöntemiyle incelenmesi (Hierarchical clustering analysis of sustainable society indicators). Üçüncü Sektör Sosyal Ekonomi Dergisi, 54(4), 2053-2073.
• GCI, Global Competitiveness Index (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. and Karakış, E. (2019). OECD ülkelerinin ekonomik özgürlüklerine göre kümeleme analizi ile sınıflandırılması. S.C.Ü. İktisadi ve İdari Bilimler Dergisi, 20(2), 1-24.
• Han, J., Kamber, M. and Pei, J. (2012). Data mining concepts and techniques. Morgan Kaufmann Publishers is An Imprint of Elsevier, Third Edition, Waltham, USA.
• IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.
• Kangallı, S. G., Uyar, U. and Buyrukoğlu, S. (2014). OECD ülkelerinde ekonomik özgürlük: bir kümeleme analizi (Economic freedom in OECD countries: A cluster analysis). Uluslararası Alanya İşletme Fakültesi Dergisi, 6(3), 95-109
• Ketchen, D. J. Jr. and Shook, C. L. (1996). The application of cluster analysis in strategic management research: an analysis and critique. Strategic Management Journal, 17, 441 -458
• Koltan Yılmaz, Ş. and Patır, S. (2011). Kümeleme analizi ve pazarlamada kullanımı (Cluster analysis and its usage in marketing). Akademik Yaklaşımlar Dergisi, 2(1), 91-113.
• Kowalski, R. and 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. and 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). Türk & İslam Dünyası Sosyal Araştırmalar Dergisi, 6(22), 219-235.
• Levy-Carciente, S., Phélan, C. M. and Perdomo, J. (2020). Prosperity in Spain and Latin America: myths and facts. International Journal of Advance Study and Research Work, 3(7), 2581-5997.
• LPI, Legatum Prosperity Index (2019a). “The Legatum Prosperity Index”, https://www.prosperity.com/about/summary/, (Accessed Date: 09.04.2020).
• LPI (2019b). “The Legatum Prosperity Index Methodology Report”, https://prosperitysite.s3-accelerate.amazonaws.com/7515/8634/9002/Methodology_for_Legatum_Prosperity_Index_2019.pdf, (Accessed Date: 09.04.2020).
• Markou, G., Palaiolouga, E., Kokkinakos, P., Markaki, O., Koussouris, S. and 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. and 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.
• Morissette, L. and Chartier, S. (2013). The K-Means clustering technique: General considerations and implementation in Mathematica, Tutorials in Quantitative Methods for Psychology, 9(1), 15-24.
• Mut, S. and 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.
• Özdamar, K. (2004). Paket Programlar İle İstatistiksel Veri Analizi 2, Eskişehir: Kaan Kitabevi.
• Peiro-Palomino J. and Picazo-Tadeo, A.J (2018). OECD: one or many? Ranking countries with a composite well-being ındicator. Soc Indic Res, 139, 847–869.
• Shahbaz, M., Iftikhar, M. and 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.
• SPI, Social Progress Index (2020). “Global Index: Overview”, https://www.socialprogress.org/index/global, (Accessed Date: 24.12.2020).
• Taşçı, M. and Özarı, Ç. (2019), OECD ülkelerinin ekonomik özgürlük göstergelerinin K-Ortalamalar kümeleme yöntemi ve Gri Ilişkisel Yöntemi ile analizi (Evaluating economic freedom criterias of OECD countries with grey relational analysis method and cluster analysis). Akademik Sosyal Araştırmalar Dergisi, 7(96), 464-488.
• 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 Ekonomi ve Yönetim Bilimleri Dergisi, 8(1), 1-18.
• Turan, K. K., Özarı, Ç. and 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). İstanbul Aydın Üniversitesi Dergisi, 29, 143-165.
• Tüzüntürk, S. (2010). Veri madenciliği ve istatistik (Data mining and statistics). Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 29(1), 65-90.
• UNDP, United Nations Development Program, HDI, Human Development Index (2019). “About Human Development”, http://hdr.undp.org/en/humandev, (Accessed Date: 13. 07. 2020).
• WHR, World Happiness Report (2020). “World Happiness Report”, https://worldhappiness.report/, (Accessed Date: 28.12.2020).