Abstract
Corruption is usually defined as the misuse of public office for private gain. Whereas the practice of corruption is probably as old as government itself, the recent emergence of more detailed measures has resulted in a considerable growth of empirical research on corruption. Furthermore, possible links between government corruption and terrorism have attracted an additional interest in this research field. Most of the existing literature discusses the topic from a socio-economical perspective and only few studies tackle research on corruption from a data mining point of view. In this chapter, we apply various 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, various predictive models 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.
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Huysmans, J., Baesens, B., Vanthienen, J. (2008). A Data Miner’s Approach to Country Corruption Analysis. In: Chen, H., Yang, C.C. (eds) Intelligence and Security Informatics. Studies in Computational Intelligence, vol 135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69209-6_13
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DOI: https://doi.org/10.1007/978-3-540-69209-6_13
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