This paper presents a subgroup discovery algorithm APRIORI-SD, developed by adapting association rule learning to subgroup 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 experimental comparisons with CN2, RIPPER and APRIORI-C demonstrate that the subgroup discovery algorithm APRIORI-SD produces substantially smaller rule sets, where individual rules have higher coverage and significance. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Kavšek, B., Lavrač, N., & Jovanoski, V. (2003). APRIORI-SD: Adapting association rule learning to subgroup discovery. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2810, 230–241. https://doi.org/10.1007/978-3-540-45231-7_22
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