ARUBAS: An Association Rule Based Similarity Framework for Associative Classifiers

Abstract

This article introduces ARUBAS, a new framework to build associative classifiers. In contrast with many existing associative classifiers, it uses class association rules to transform the feature space and uses instance-based reasoning to classify new instances. The framework allows the researcher to use any association rule mining algorithm to produce the class association rules. Every aspect of the framework is extensively introduced and discussed and five different fitness measures used for classification purposes are defined. The empirical results determine which fitness measure is the best and compares the framework with other classifiers. These results show that the ARUBAS framework is able to produce associative classifiers which are competitive with other classification techniques. More specifically, with ARUBAS-Scheffer-phi5 we have introduced a parameter-free algorithm which is competitive with classification techniques such as C4.5, RIPPER and CBA.