Full Citation

Title: Statistical Strategies for Pruning All the Uninteresting Association Rules

Citation Type: Conference Paper

Publication Year: 2004

ISBN: 9781586034528

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Abstract: We propose a general framework to formalize the problem 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 . . .

Url: https://books.google.com/books?hl=en&lr=&id=rU_onmzozu0C&oi=fnd&pg=PA430&ots=w7nr74X4Ya&sig=k07RD4PNBjPU9mv_RzdKRcmSIBg#v=onepage&q=IPUMS OR %22Integrated Public Use%22 OR NHGIS OR %22National Historical Geographic Information%22 O

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Authors: Casas-Garriga, Gemma

Conference Name: ECAI 2004: 16th European Conference on Artificial Intelligence

Publisher Location: Valencia, Spain

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Other

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