Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Exploring labeled spatial datasets using association analysis
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
ESTATE: strategy for exploring labeled spatial datasets using association analysis
DS'10 Proceedings of the 13th international conference on Discovery science
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Association analysis provides a natural, data-centric framework for the discovery of patterns of explanatory variables that are linked to a certain outcome. In this paper we demonstrate how such a framework can be applied for political analysis, using an expository example of discovering different spatio-social motifs of support for Barack Obama in the 2008 presidential election. Election results and thirteen different socio-economic explanatory variables, tabulated at the county level, are used as an input for calculating a collection of discriminative patterns having disproportionately large support within the counties won by Obama. These patterns are synthesized into a small number of larger socio-economics motifs using a novel pattern similarity measure that outputs a concise summary readily interpretable in terms of political analysis. The method discovers two major Obama constituencies that differ in their socio-economic makeup and in their geographical distributions. The larger constituency can be further divided into more narrowly-defined motifs.