Clustering of Member and Candidate Countries of the European Union

Hasan Bulut, Yüksel Öner, Çağlar Sözen


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. 


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

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