An Efficient Method for Mining Informative Association Rules in Knowledge Extraction

Autor: Parfait Bemarisika, André Totohasina

Publikováno v: Lecture Notes in Computer Science
4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2020, Dublin, Ireland. pp.227-247, ⟨10.1007/978-3-030-57321-8_13⟩
Lecture Notes in Computer Science ISBN: 9783030573201
CD-MAKE

International audience; Mining association rules is an important problem in Knowledge Extraction (KE). This paper proposes an efficient method for mining simultaneously informative positive and negative association rules, using a new selective pair s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::20e60560f3e79ab09da937072936c40d
https://hal.inria.fr/hal-03414741

ERAPN, An Algorithm for Extraction Positive and Negative Association Rules in Big Data

Autor: André Totohasina, Parfait Bemarisika

Publikováno v: Proceedings of the International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2018
International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2018
International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2018, Sep 2018, Regensburg, Germany. pp.329-344
Big Data Analytics and Knowledge Discovery ISBN: 9783319985381
DaWaK

Over the past two decades, the extraction of positive association rules is a significant research area in Big Data. While the negative association rules has been received a lot of attention, they remained in the shadows due to the difficulty of their

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::682aa14e7094ad64c4f3ef9ae07ae84d
https://hal.univ-reunion.fr/hal-01910862