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Peter Sarlin
''Evaluating a Self-Organizing Map for Clustering and Visualizing Optimum Currency Area Criteria''
( 2011, Vol. 31 No.2 )
Optimum currency area (OCA) theory attempts to define the geographical region in which it would maximize economic efficiency to have a single currency. In this paper, the focus is on prospective and current members of the Economic and Monetary Union. For this task, a self-organizing neural network, the Self-organizing map (SOM), is combined with hierarchical clustering for a two-level approach to clustering and visualizing OCA criteria. The output of the SOM is a topologically preserved two-dimensional grid. The final models are evaluated based on both clustering tendencies and accuracy measures. Thereafter, the two-dimensional grid of the chosen model is used for visual assessment of the OCA criteria, while its clustering results are projected onto a geographic map.
Keywords: Self-organizing maps, Optimum Currency Area, projection, clustering, geospatial visualization
Manuscript Received : Nov 24 2010 Manuscript Accepted : May 22 2011

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