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Evaluating a Self-Organizing Map for Clustering and Visualizing Optimum Currency Area Criteria

Peter Sarlin, Evaluating a Self-Organizing Map for Clustering and Visualizing Optimum Currency Area Criteria. Economics Bulletin 31(2), 1483–1495, 2011.

Abstract:

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.

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BibTeX entry:

@ARTICLE{jSa11b,
  title = {Evaluating a Self-Organizing Map for Clustering and Visualizing Optimum Currency Area Criteria},
  author = {Sarlin, Peter},
  journal = {Economics Bulletin},
  volume = {31},
  number = {2},
  pages = {1483–1495},
  year = {2011},
  keywords = {Self-organizing maps; Optimum Currency Area; projection; clustering; geospatial visualization},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

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