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Online numerical association rule miner

Abstract: Green AI refers to those AI methods that are friendly to the environment, i.e., are capable to keep the consumption of electrical energy at a minimum. In this sense, a new numerical association rule miner is proposed that presents a combination of the already existing offline uARMSolver, belonging to a Red AI class, and a newly developed onlineNARM miner representing the new Green AI. The former is devoted to exhaustive search of the evolutionary solution space, while the latter for faster exploiting of already explored search space. The experimental results on four transaction databases showed that, by sacrificing the quality of the results by 0.7-% by the onlineNARM we can obtain the results almost 85.0-% faster than with the uARMSolver in the best test scenario.

Other publications of the same journal or congress with authors from the University of Cantabria

 Authorship: Fister I., Iglesias A., Galvez A., Fister I.,

 Fuente: Neurocomputing, 2023, 528, 33-43

Publisher: Elsevier

 Publication date: 28/02/2023

No. of pages: 11

Publication type: Article

 DOI: 10.1016/j.neucom.2022.12.002

ISSN: 0925-2312,1872-8286

 Spanish project: PID2021-127073OB-I00

 European project: info:eu-repo/grantAgreement/EC/H2020/778035/EU/PDE-based geometric modelling, image processing, and shape reconstruction/PDE-GIR/

Publication Url: https://doi.org/10.1016/j.neucom.2022.12.002

Authorship

FISTER, IZTOK

FISTER, IZTOK, JR.