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

14 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

Cite this

Grosskreutz, H., Boley, M., & Krause-Traudes, M. (2010). Subgroup discovery for election analysis: A case study in descriptive data mining. In Discovery Science - 13th International Conference, DS 2010, Proceedings (pp. 57-71). (Lecture Notes in Computer Science; Vol. 6332 ). Springer. https://doi.org/10.1007/978-3-642-16184-1_5
Grosskreutz, Henrik ; Boley, Mario ; Krause-Traudes, Maike. / Subgroup discovery for election analysis : A case study in descriptive data mining. Discovery Science - 13th International Conference, DS 2010, Proceedings. Springer, 2010. pp. 57-71 (Lecture Notes in Computer Science).
@inproceedings{5665f389c0ce4f558c5b84fca91b5bb1,
title = "Subgroup discovery for election analysis: A case study in descriptive data mining",
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.",
author = "Henrik Grosskreutz and Mario Boley and Maike Krause-Traudes",
year = "2010",
doi = "10.1007/978-3-642-16184-1_5",
language = "English",
isbn = "3642161839",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "57--71",
booktitle = "Discovery Science - 13th International Conference, DS 2010, Proceedings",

}

Grosskreutz, H, Boley, M & Krause-Traudes, M 2010, Subgroup discovery for election analysis: A case study in descriptive data mining. in Discovery Science - 13th International Conference, DS 2010, Proceedings. Lecture Notes in Computer Science, vol. 6332 , Springer, pp. 57-71, 13th International Conference on Discovery Science, DS 2010, Canberra, ACT, Australia, 6/10/10. https://doi.org/10.1007/978-3-642-16184-1_5

Subgroup discovery for election analysis : A case study in descriptive data mining. / Grosskreutz, Henrik; Boley, Mario; Krause-Traudes, Maike.

Discovery Science - 13th International Conference, DS 2010, Proceedings. Springer, 2010. p. 57-71 (Lecture Notes in Computer Science; Vol. 6332 ).

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

TY - GEN

T1 - Subgroup discovery for election analysis

T2 - A case study in descriptive data mining

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 Paper

AN - SCOPUS:78650134994

SN - 3642161839

SN - 9783642161834

T3 - Lecture Notes in Computer Science

SP - 57

EP - 71

BT - Discovery Science - 13th International Conference, DS 2010, Proceedings

PB - Springer

ER -

Grosskreutz H, Boley M, Krause-Traudes M. Subgroup discovery for election analysis: A case study in descriptive data mining. In Discovery Science - 13th International Conference, DS 2010, Proceedings. Springer. 2010. p. 57-71. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-16184-1_5