Polynomial association rules with applications to logistic regression

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. Categories and Subject Descriptors H.2.8 [Database Management]: Database ApplicationsData Mining General Terms Algorithms, Experimentation, Performance Keywords Association rules, continuous attributes

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Szymon Jaroszewicz
• Keywords: Data Mining, KDD 2006, Keywords Association Rules, Polynomial Association Rules, Polynomial Associations