Clustering of Member and Candidate Countries of the European Union

Hasan Bulut, Yüksel Öner, Çağlar Sözen

Abstract


The clustering analysis aims to classify multivariate observations. For this, it uses any similarity or difference measures. In literature, clustering analysis is used to classify countries in many studies. In this study, we aim to classify the EU Member and Candidate Countries by cluster analysis in terms of some economic variables and to reveal the similarities of candidate and member countries. We have used Ward Algorithm which is a hierarchical cluster method and k-means Algorithm that is a non-hierarchical cluster method. Moreover, we have used clustering validation indexes for comparison of clustering results. To this aim, Dunn, Connectivity and Silhouette indexes are preferred as clustering validation indexes. 


Keywords


European Union; K Means; Ward; Cluster Algorithm; Cluster Validation Indexes.

Full Text:

PDF

References


https://europa.eu/european-union/index_en

Turanlı, M., Özden, Ü. H. and Türedi, S. “Analysis of the economical similarities of European Union members and candidate countries with cluster analysis”. İstanbul Trade University Social Sciences Journal, 5.9:95-109, 2006.

Ersöz, F. “Comparison of the Selected Health Indicators of OECD Member Countries with Cluster and Discriminant Analysis”. Journal of Medical Sciences, 29.6:1650-1659, 2009.

Akın, H. B., & Özge, E. “OECD Countries With Education Indicators Comparative Analysis of Cluster Analysis and Multi-Dimensional Scaling Analysis”. Proposal Journal, 10.37:175-181, 2012.

Aykın, S. M. and Korkmaz, A. “Clustering Turkey and the Member States in Terms of EU-2020 Strategy Indicators”. ESOGÜ Journal of Faculty of Economics and Administrative Sciences, 9.1:7-20, 2014.

Tekin, B. “Grouping of cities in terms of primary health indicators in Turkey: an application of cluster analysis”. Journal of Karatekin University Faculty of Economics and Administrative Sciences, 5.2:389-417, 2015.

Atal, S. “Fuzzy Clustering Analyze and clustering OECD Countries in development”. ESOGÜ Journal of Institute of Science, 2015.

Turan, K. K., Özarı, Ç., “Comparing Turkey and The Middle East Countries with Cluster Analysis: Economic Perspective”. İstanbul Aydın University, 29: 143-165, 2016.

Tatlıdil, H. Applied Multivariate Statistical Analyze. Ankara: Academy Publishing, 1996.

Rencher, A. C. Methods of Multivariate Analysis. A John Wiley & Sons, Inc. Publication, 2002.

Aggarwal, C.C., Reddy, C. K. Data Clustering Algorithms and Applications. Boca Raton: CRC Press, 2014.

Brock, G., Pihır, V., Datta, S., Datta, S. “clValid: An R Package for Cluster Validation”. Journal of Statistical Software, 25.4:1-22, 2008.

https://cran.r-project.org/web/packages/cluster/cluster.pdf

http://www.worldbank.org/

Alpar, R. Applied Multivariate Statistical Methods. Ankara: Detay Publishing, 2013.


Refbacks

  • There are currently no refbacks.


 

 
  
 

 

  


About IJSBAR | Privacy PolicyTerms & Conditions | Contact Us | DisclaimerFAQs 

IJSBAR is published by (GSSRR).