Threshold tuning for improved classification association rule mining

Authors:
Frans Coenen;Paul Leng;Lu Zhang
Affiliations:
Department of Computer Science, The University of Liverpool, Liverpool, UK;Department of Computer Science, The University of Liverpool, Liverpool, UK;Department of Computer Science and Technology, Peking University, Beijing, P.R. China
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

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Abstract

One application of Association Rule Mining (ARM) is to identify Classification Association Rules (CARs) that can be used to classify future instances from the same population as the data being mined. Most CARM methods first mine the data for candidate rules, then prune these using coverage analysis of the training data. In this paper we describe a CARM algorithm that avoids the need for coverage analysis, and a technique for tuning its threshold parameters to obtain more accurate classification. We present results to show this approach can achieve better accuracy than comparable alternatives at lower cost.