Tech Reports

ULCS-07-020

A Novel Rule Weighting Approach in Classification Association Rule Mining (An Extended Version of 2007 IEEE ICDM Workshop Paper)

Yanbo J. Wang, Qin Xin and Frans Coenen

Abstract

Classification Association Rule Mining (CARM) is a recent Classification Rule Mining (CRM) approach that builds an Association Rule Mining (ARM) based classifier using Classification Association Rules (CARs). Regardless of which particular CARM algorithm is used, a similar set of CARs is always generated from data, and a classifier is usually presented as an ordered list of CARs, based on a selected rule ordering strategy. Hence to produce an accurate classifier, it is essential to develop a rational rule ordering mechanism. In the past decade, a number of rule ordering strategies have been introduced that can be categorized under three headings: (1) support-confidence, (2) rule weighting, and (3) hybrid. In this paper, we propose an alternative rule weighting scheme, namely CISRW (Class-Item Score based Rule Weighting), and develop a rule weighting based rule ordering mechanism based on CISRW. Subsequently, two hybrid rule ordering strategies are further introduced by combining (1) and CISRW. The experimental results show that the three proposed CISRW based/related rule ordering strategies perform well with respect to the accuracy of classification.

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