Analysis of UN Voting Patterns via Diffusion Geometry and Thematic Clustering

Minh-Tam Le, Mathew Lawlor, Bruce M. Russett, John Sweeney, Steven W. Zucker
2013 2013 IEEE 13th International Conference on Data Mining Workshops  
We apply a range of data mining techniques to analyze voting patterns in the United Nations. We begin with nonlinear dimensionality reduction, showing that diffusion geometry reveals an historically relevant organization of countries based on their UN voting patterns. Key historical events can be "read out" from these embeddings, such as de Gaulle's influence on France and the breakup of the Soviet Union. These events are not apparent in other (e.g., PCA) embeddings. We then switch to an
more » ... switch to an organization of resolutions, revealing dominant themes during different political epochs. Formally themes are introduced as summaries (eigenfunctions) within a modified hierarcical clustering algorithm.
doi:10.1109/icdmw.2013.81 dblp:conf/icdm/LeLRSZ13 fatcat:dg3ywr2wp5gjfpexprmzrtkjti