ECAI 2004 Conference Paper

[full paper]

Statistical Strategies for Pruning All the Uninteresting Association Rules

Gemma Casas-Garriga

We propose a general framework to formalize the pro\-blem of capturing the intensity of implication for association rules through statistical metrics. In this framework we present properties that influence the interestingness of a rule, analyze the conditions that lead a measure to perform a perfect prune at a time, and define a final proper order to sort the surviving rules. We will discuss why none of the currently employed measures can capture objective interestingness, and just the combination of some of them, in a multi-step fashion, can be reliable. In contrast, we propose a new simple modification of the Pearson coefficient that will meet all the necessary requirements. We statistically infer the convenient cut-off threshold for this new metric by empirically describing its distribution function through simulation. Final experiments show the ability of our proposal.

Keywords: Association Rules, Statistical metrics, Pruning Strategy, Order on Rules

Citation: Gemma Casas-Garriga: Statistical Strategies for Pruning All the Uninteresting Association Rules. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.430-434.