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Dimensionality Reduction and Visualisation Tools for Voting Record

Author(s)
Brigadir, Igor  
Greene, Derek  
Cross, James P.  
Cunningham, Pádraig  
Uri
http://hdl.handle.net/10197/8316
Date Issued
2016-09-21
Date Available
2017-02-03T15:44:50Z
Abstract
Recorded votes in legislative bodies are an important source of data for political scientists. Voting records can be used to describe parliamentary processes, identify ideological divides between members and reveal the strength of party cohesion. We explore the problem of working with vote data using popular dimensionality reduction techniques and cluster validation methods, as an alternative to more traditional scaling techniques. We present results of dimensionality reduction techniques applied to votes from the 6th and 7th European Parliaments, covering activity from 2004 to 2014.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
CEUR Workshop Proceedings
Copyright (Published Version)
2016 the Authors
Subjects

Machine learning

Statistics

Voting records

Web versions
http://ceur-ws.org/Vol-1751/
Language
English
Status of Item
Peer reviewed
Journal
Greene, D., Mac Namee, B. and Ross, R. (eds.). Proceedings of the 24th Irish Conference on Artificial Intelligence and Cognitive Science
Conference Details
24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16), University College Dublin, Ireland, 20-21 September 2016
ISSN
1613-0073
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
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insight_publication.pdf

Size

298.06 KB

Format

Adobe PDF

Checksum (MD5)

626e230a14158e0983ed87da9f227e62

Owning collection
Insight Research Collection
Mapped collections
Politics and International Relations Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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