JSM 2004 - Toronto

Abstract #301080

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Activity Number: 425
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #301080
Title: A Statistical Foundation for Association Rules Based on Clustering
Author(s): Carlos Ordonez*+
Companies: Teradata, NCR
Address: 17095 Via del Campo, San Diego, CA, 92127,
Keywords: association rules ; binary data ; clustering ; support bounds
Abstract:

Association rules are a data-mining technique for which there exist efficient algorithms, but that as of today lack an adequate statistical foundation. Therefore, we propose a statistical model for association rules. This approach is instrumental in gaining a deeper understanding of association rules based on a simple, yet enlightening, statistical model. A dataset with binary variables is modeled as a set of clusters. Such binary data clusters are used to get tight lower and upper bounds on association support. Clusters are also used to get lower and upper bounds on association rule confidence. Then bound averages are used to estimate support and confidence. The model estimates show asymptotic increasing accuracy as the number of clusters increases. We discuss our progress on proving theoretical guarantees provided by the model.

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