Richard Khoury

Membre affilié

Professeur agrégé, Université Laval, Département d'informatique et de génie logiciel

Sujets de recherche

Analyse de données

Modèles génératifs

Traitement du langage naturel

Site web

Google Scholar

Publications

Association Rules Mining with Auto-Encoders

Th'eophile Berteloot

Richard Khoury

Audrey Durand

Association rule mining is one of the most studied research fields of data mining, with applications ranging from grocery basket problems to… (voir plus) explainable classification systems. Classical association rule mining algorithms have several limitations, especially with regards to their high execution times and number of rules produced. Over the past decade, neural network solutions have been used to solve various optimization problems, such as classification, regression or clustering. However there are still no efficient way association rules using neural networks. In this paper, we present an auto-encoder solution to mine association rule called ARM-AE. We compare our algorithm to FP-Growth and NSGAII on three categorical datasets, and show that our algorithm discovers high support and confidence rule set and has a better execution time than classical methods while preserving the quality of the rule set produced.

2023-04-26

ArXiv (prépublication)

doi.org

arxiv.org

Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy

Alexandre Larouche

Audrey Durand

Richard Khoury

Caroline Sirois

2022-12-10

ArXiv (prépublication)

doi.org