TY - GEN
T1 - Subgroup discovery for election analysis
T2 - 13th International Conference on Discovery Science, DS 2010
AU - Grosskreutz, Henrik
AU - Boley, Mario
AU - Krause-Traudes, Maike
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78650134994&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-16184-1_5
DO - 10.1007/978-3-642-16184-1_5
M3 - Conference contribution
AN - SCOPUS:78650134994
SN - 3642161839
SN - 9783642161834
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 57
EP - 71
BT - Discovery Science - 13th International Conference, DS 2010, Proceedings
Y2 - 6 October 2010 through 8 October 2010
ER -