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

18Citations
Citations of this article
10Readers
Mendeley users who have this article in their library.
Get full text

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. © 2010 Springer-Verlag.

Cite

CITATION STYLE

APA

Grosskreutz, H., Boley, M., & Krause-Traudes, M. (2010). Subgroup discovery for election analysis: A case study in descriptive data mining. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6332 LNAI, pp. 57–71). https://doi.org/10.1007/978-3-642-16184-1_5

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free