Deterministic Extraction of Compact Sets of Rules for Subgroup Discovery
This work presents a novel deterministic method to obtain rules for Subgroup Discovery tasks. It makes no previous discretization for the numeric attributes, but their conditions are obtained dynamically. To obtain the final rules, the AUC value of a rule has been used for selecting them. An experimental study supported by appropriate statistical tests was performed, showing good results in comparison with the classic deterministic algorithms CN2-SD and APRIORI-SD. The best results were obtained in the number of induced rules, where a significant reduction was achieved. Also, better coverage and less number of attributes were obtained in the comparison with CN2-SD.
KeywordsData mining Machine learning Rule-based systems
This work was partially funded by the Regional Government of Andalusia (Junta de Andalucía), grant number TIC-7629.
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