Fuzzy Clustering Techniques for Optimum Currency Area Variables

Through this paper, we attempt to cluster the various Economic and Monetary Union (EMU) according to the different Optimum Currency Area (OCA) variables and demonstrate their similarity to various other countries. We utilize three different fuzzy clustering techniques for the process; thus, determining the different clusters these countries should be grouped in as suggested by the OCA variables. The reason for taking fuzzy clustering is that fuzzy clustering allows the data to be part of one or more clusters as opposed to hard clustering which limits the data to only one cluster. This study extends the studies of Itir Ozer and Ibrahim Ozkan (2008) .We use the same data for the analysis. We also carried out the cluster validity utilizing four different indices to get the accurate number of clusters and Principal Component Analysis (PCA) to get a more comprehensive view of the data.