Polynomial association rules with applications to logistic regression

Authors:
Szymon Jaroszewicz
Affiliations:
Szczecin University of Technology, Szczecin, Poland & National Institute of Telecommunications, Warsaw, Poland
Venue:
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2006

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Abstract

A new class of associations (polynomial itemsets and polynomial association rules) is presented which allows for discovering nonlinear relationships between numeric attributes without discretization. For binary attributes, proposed associations reduce to classic itemsets and association rules. Many standard association rule mining algorithms can be adapted to finding polynomial itemsets and association rules. We applied polynomial associations to add non-linear terms to logistic regression models. Significant performance improvement was achieved over stepwise methods, traditionally used in statistics, with comparable accuracy.