APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery

Abstract. This paper presents a subgroup discovery algorithm APRIORI-SD, developed by adapting association rule learning to sub-group discovery. This was achieved by building a classification rule learner APRIORI-C, enhanced with a novel post-processing mechanism, a new quality measure for induced rules (weighted relative accuracy) and using probabilistic classification of instances. Results of APRIORI-SD are similar to the subgroup discovery algorithm CN2-SD while experi-mental comparisons with CN2, RIPPER and APRIORI-C demonstrate that the subgroup discovery algorithm APRIORI-SD produces substan-tially smaller rule sets, where individual rules have higher coverage and significance.