Subgroup discovery for election analysis: a case study in descriptive data mining

Authors:
Henrik Grosskreutz;Mario Boley;Maike Krause-Traudes
Affiliations:
Fraunhofer IAIS, Schloss Birlinghoven, Sankt Augustin, Germany;Fraunhofer IAIS, Schloss Birlinghoven, Sankt Augustin, Germany;Fraunhofer IAIS, Schloss Birlinghoven, Sankt Augustin, Germany
Venue:
DS'10 Proceedings of the 13th international conference on Discovery science
Year:
2010

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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.