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

Henrik Grosskreutz, Mario Boley, Maike Krause-Traudes

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

16 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationDiscovery Science - 13th International Conference, DS 2010, Proceedings
PublisherSpringer
Pages57-71
Number of pages15
ISBN (Print)3642161839, 9783642161834
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event13th International Conference on Discovery Science, DS 2010 - Canberra, ACT, Australia
Duration: 6 Oct 20108 Oct 2010

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume6332
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Discovery Science, DS 2010
CountryAustralia
CityCanberra, ACT
Period6/10/108/10/10

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