Abstract
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.
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References
Gerring, J., Thacker, S.: political institutions and corruption: The role of unitarism and parliamentarism. The British Journal of Political Science 34, 295–330 (2004)
Lambsdorff, J.: Corruption in empirical research: a review. Transparency International Working paper (1999)
Bohara, A., Mitchell, N., Mittendorff, C.: Compound democracy and the control of corruption: A cross-country investigation. The Policy Studies Journal 32(4), 481–499 (2004)
Treisman, D.: The causes of corruption: a cross-national study. Journal of Public Economics 76(3), 339–457 (2000)
Leite, C., Weidmann, J.: Does mother nature corrupt? natural resources, corruption and economical growth. International Monetary Fund Working Paper 99(85) (1999)
Swamy, A., Knack, S., Lee, Y., Azfar, O.: Gender and corruption. Journal of Development Economics 64, 25–55 (2001)
Alesina, A., Weder, B.: Do corrupt governments receive less foreign aid? National Bureau of Economic Research Working Paper 7108 (1999)
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., Vanthienen, J.: Benchmarking state of the art classification algorithms for credit scoring. Journal of the Operational Research Society 54(6), 627–635 (2003)
Suykens, J., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific, Singapore (2002)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 43, 59–69 (1982)
Vesanto, J.: Som-based data visualization methods. Intelligent Data Analysis 3, 111–126 (1999)
Honkela, T., Kaski, S., Lagus, K., Kohonen, T.: WEBSOM—self-organizing maps of document collections. In: Proceedings of WSOM 1997, Workshop on Self-Organizing Maps, Helsinki University of Technology, Neural Networks Research Centre, Espoo, Finland, June 4-6, pp. 310–315 (1997)
Brockett, P., Xia, X., Derrig, R.: Using kohonen’s self-organizing feature map to uncover automobile bodily injury claims fraud. International Journal of Risk and Insurance 65, 245–274 (1998)
Kohonen, T.: Self-Organising Maps. Springer, Heidelberg (1995)
Deboeck, G., Kohonen, T.: Visual Explorations in Finance with selforganizing maps. Springer, Heidelberg (1998)
Transparency International, http://www.transparency.org/
Freedom House: Freedom in the world country ratings (2005)
Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)
Azcarraga, A., Hsieh, M., Pan, S., Setiono, R.: Extracting salient dimensions for automatic som labeling. Transactions on Systems, Management and Cybernetics, Part C 35(4), 595–600 (2005)
Lagus, K., Kaski, S.: Keyword selection method for characterizing text document maps. In: Proceedings of ICANN 1999, Ninth International Conference on Artificial Neural Networks, pp. 371–376. IEE (1999)
Rauber, A., Merkl, D.: Automatic labeling of self-organizing maps: Making a treasure-map reveal its secrets. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 228–237. Springer, Heidelberg (1999)
Montinola, G., Jackman, R.: Sources of corruption: a cross-country study. British Journal of Political Science 32, 147–170 (2002)
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Huysmans, J., Martens, D., Baesens, B., Vanthienen, J., Van Gestel, T. (2006). Country Corruption Analysis with Self Organizing Maps and Support Vector Machines. In: Chen, H., Wang, FY., Yang, C.C., Zeng, D., Chau, M., Chang, K. (eds) Intelligence and Security Informatics. WISI 2006. Lecture Notes in Computer Science, vol 3917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11734628_13
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DOI: https://doi.org/10.1007/11734628_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33361-6
Online ISBN: 978-3-540-33362-3
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