Using Self Organizing Maps to Analyze Demographics and Swing State Voting in the 2008 U.S. Presidential Election
Lecture Notes in Artificial Intelligence: ANNPR 2012
Emergent self-organizing maps (ESOMs) and k-means clustering are used to cluster counties in each of the states of Florida, Pennsylvania, and Ohio by demographic data from the 2010 United States census. The counties in these clusters are then analyzed for how they voted in the 2008 U.S. Presidential election, and political strategies are discussed that target demographically similar geographical regions based on ESOM results. The ESOM and k-means clusterings are compared and found to be dissimilar by the variation of information distance function.
Kohonen self organizing map, k-means clustering, variation of information, United States election 2008, United States Census data 2010
Pearson, Paul T. and Cameron I. Cooper. Using Self Organizing Maps to Analyze Demographics and Swing State Voting in the 2008 U.S. Presidential Election. Vol. 7477 Lecture Notes in Artificial Intelligence: Annpr 2012, 2012.
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Published in the proceedings of the 5th Workshop on Artifical Neural Networks in Pattern Recognition and will appear in N. Mana, F. Schwenker, and E. Trentin (Eds.): ANNPR 2012, Lecture Notes in Artificial Intelligence 7477, pp. 201–212. Springer, Heidelberg (2012).