Comparer des méthodes

	Règles d'association	Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1993	1990–1997
Auteur d'origine	Agrawal, R., Imielinski, T., & Swami, A.	Schapire, R. E.; Freund, Y.
Type	Unsupervised pattern discovery	Sequential ensemble (iterative reweighting)
Source fondatrice	Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
Alias	market basket analysis, association rule mining, frequent itemset mining, affinity analysis	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Apparentées	4	6
Résumé	Association rule learning is an unsupervised technique that discovers co-occurrence patterns — 'if X then Y' implications — within large transactional datasets. Originally formalized by Agrawal, Imielinski, and Swami (1993) for supermarket basket analysis, it is now widely applied in e-commerce recommendation, health informatics, bioinformatics, and behavioral research.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.