Abstract
Clustering of countries and economies has been done for a long time by expert comparing to ideal theoretical entities. More recently a data driven approach has been taken, including, so called, black box methods. In this paper a SOM-based dissimilarity measure is presented and used for agglomerative hierarchical clustering of economies. It turns out that the results differ significantly from those obtained via a more traditional Euclidean distance based approach.
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The author would like to thank Prof. Michal Ramsza for critical reading of the manuscript.
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Chudziak, A. (2016). Economies Clustering Using SOM-Based Dissimilarity. In: Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2016. Communications in Computer and Information Science, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-319-44188-7_8
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DOI: https://doi.org/10.1007/978-3-319-44188-7_8
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