Abstract #301971

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JSM 2003 Abstract #301971
Activity Number: 291
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: Business & Economics Statistics Section
Abstract - #301971
Title: A Real-World Implementation of the Binomial Test for Determining the Significance of Association Rules
Author(s): Robert G. Sievert*+
Companies: NCR Data Mining Lab
Address: 110 Maple Tree Ave. 2C, Stamford, CT, 06906-2244,
Keywords: data mining ; association rules ; binomial test
Abstract:

Identification of "meaningful" association rules in large real-world datasets is an issue with substantial practical implementations for classic data-mining algorithms. The generation of rules is one of the more well-studied problems in data mining, but most studies center on creating more efficient algorithms for generating association rules, and not on evaluation of the significance of those rules. Support constraint selection to limit the number of candidate item-sets serves to limit the final number of generated association rules; but in real-world datasets, the volume of generated rules is still massive enough to require additional criteria to help the user of those rules separate meaningful ones from happenstance. A smaller group of studies have addressed the "interestingness" of association rules, some using lift or "conviction," and one applying a chi-square test to the generated rules. This paper presents, with examples, a real-world implementation of the binomial test for evaluating the significance of association rules, underscoring the bridge between statistical tests and knowledge discovery in databases.

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