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Subgroup Discovery for Election Analysis: A Case Study in Descriptive Data Mining

  • Henrik Grosskreutz
  • Mario Boley
  • Maike Krause-Traudes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6332)

Abstract

In this paper, we investigate the application of descriptive data mining techniques, namely subgroup discovery, for the purpose of the ad-hoc analysis of election results. Our inquiry is based on the 2009 German federal Bundestag election (restricted to the City of Cologne) and additional socio-economic information about Cologne’s polling districts. The task is to describe relations between socio-economic variables and the votes in order to summarize interesting aspects of the voting behavior. Motivated by the specific challenges of election data analysis we propose novel quality functions and visualizations for subgroup discovery.

Keywords

High Share Quality Function Target Variable Election Result Grand Coalition 
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 2010

Authors and Affiliations

  • Henrik Grosskreutz
    • 1
  • Mario Boley
    • 1
  • Maike Krause-Traudes
    • 1
  1. 1.Fraunhofer IAISSchloss BirlinghovenSankt AugustinGermany

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