Country Corruption Analysis with Self Organizing Maps and Support Vector Machines

  • Johan Huysmans
  • David Martens
  • Bart Baesens
  • Jan Vanthienen
  • Tony Van Gestel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3917)


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.


Support Vector Machine Civil Liberty Corruption Perception Index Component Plane Best Match Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Johan Huysmans
    • 1
  • David Martens
    • 1
  • Bart Baesens
    • 1
    • 2
  • Jan Vanthienen
    • 1
  • Tony Van Gestel
    • 3
    • 4
  1. 1.Department of Decision Sciences and Information ManagementKatholieke Universiteit LeuvenLeuvenBelgium
  2. 2.School of ManagementUniversity of SouthamptonSouthamptonUnited Kingdom
  3. 3.Dexia GroupCredit Risk ModellingBrusselsBelgium
  4. 4.Department of Electrical EngineeringESAT-SCD-SISTA, Katholieke Universiteit LeuvenLeuven (Heverlee)Belgium

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