A Novel Rule Weighting Approach in Classification Association Rule Mining

Abstract

Classification association rule mining (CARM) is a recent classification rule mining approach that builds an association rule mining 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 CAR list, based on a selected rule ordering strategy. 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 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.