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Title: Statistical Strategies for Pruning All the Uninteresting Association Rules

Citation Type: Miscellaneous

Publication Year: 2004

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Abstract: 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 inte- restingness, and just the combination of some of them in a multi-step fashion, can be reliable. In contrast, we propose a new simple mo- dification 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. Experiments show a promising behaviour of our proposal.

Url: http://researchers.lille.inria.fr/~garriga/papers/ecai04.ps

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Authors: Garriga, Gemma, C

Publisher: IOS Press

Data Collections: IPUMS USA

Topics: Population Data Science

Countries: United States