Dimensionality Reduction and Visualisation Tools for Voting Record
|Title:||Dimensionality Reduction and Visualisation Tools for Voting Record||Authors:||Brigadir, Igor
Cross, James P.
|Permanent link:||http://hdl.handle.net/10197/8316||Date:||21-Sep-2016||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.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||CEUR Workshop Proceedings||Copyright (published version):||2016 the Authors||Keywords:||Machine learning; Statistics; Voting records||Other versions:||http://ceur-ws.org/Vol-1751/||Language:||en||Status of Item:||Peer reviewed||Is part of:||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|
|Appears in Collections:||Politics and International Relations Research Collection|
Insight Research Collection
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