Self-organizing maps
Visual Explorations in Finance
Visual Explorations in Finance
Automatic Labeling of Self-Organizing Maps: Making a Treasure-Map Reveal Its Secrets
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Extracting salient dimensions for automatic SOM labeling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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During recent years, the empirical research on corruption has grown considerably. Possible links between government corruption and terrorism have attracted an increasing interest in this research field. Most of the existing literature discusses the topic from a socio-economical perspective and only few studies tackle this research field from a data mining point of view. In this paper, we apply data mining techniques onto a cross-country database linking macro-economical variables to perceived levels of corruption. In the first part, self organizing maps are applied to study the interconnections between these variables. Afterwards, support vector machines are trained on part of the data and used to forecast corruption for other countries. Large deviations for specific countries between these models' predictions and the actual values can prove useful for further research. Finally, projection of the forecasts onto a self organizing map allows a detailed comparison between the different models' behavior.