Approximate Association Rule Mining

Abstract

Association rule algorithms typically only identify patterns that occur in the original form throughout the database. In databases which contain many small variations in the data, potentially important discoveries may be ignored as a result. In this paper, we describe an associate rule mining algorithm that searches for approximate association rules. Our ~AR approach allows data that approximately matches the pattern to contribute toward the overall support of the pattern. This approach is also useful in processing missing data, which probabilistically contributes to the support of possibly matching patterns. Results of the ~AR algorithm are demonstrated using the Weka system and sample databases. Contact Author: Diane J. Cook Department of Computer Science and Engineering Box 19015 University of Texas at Arlington Arlington, TX 76019 Office: (817) 272-3606 Fax: (817) 272-3784 Email: cook@cse.uta.edu Keywords: knowledge discovery, association rules, inexact match, missing data 2 Abstract Association rule algorithms typically only identify patterns that occur in the original form throughout the database. In databases which contain many small variations in the data, potentially important discoveries may be ignored as a result. In this paper, we describe an associate rule mining algorithm that searches for approximate association rules. Our ~AR approach allows data that approximately matches the pattern to contribute toward the overall support of the pattern. This approach is also useful in processing missing data, which probabilistically contributes to the support of possibly matching patterns. Results of the ~AR algorithm are demonstrated using the Weka system and sample databases.