TRARM-RelSup: targeted rare association rule mining using itemset trees and the relative support measure

Authors:
Jennifer Lavergne;Ryan Benton;Vijay V. Raghavan
Affiliations:
The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA;The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA;The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA
Venue:
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Year:
2012

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Abstract

The goal of association mining is to find potentially interesting rules in large repositories of data. Unfortunately using a minimum support threshold, a standard practice to improve the association mining processing complexity, can allow some of these rules to remain hidden. This occurs because not all rules which have high confidence have a high support count. Various methods have been proposed to find these low support rules, but the resulting increase in complexity can be prohibitively expensive. In this paper, we propose a novel targeted association mining approach to rare rule mining using the itemset tree data structure (aka TRARM-RelSup). This algorithm combines the efficiency of targeted association mining querying with the capabilities of rare rule mining; this results in discovering a more focused, standard and rare rules for the user, while keeping the complexity manageable.