Comparer des méthodes

	Algorithme Apriori	Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1994	1990–1997
Auteur d'origine	Agrawal, R. & Srikant, R.	Schapire, R. E.; Freund, Y.
Type	Frequent itemset and association rule mining algorithm	Sequential ensemble (iterative reweighting)
Source fondatrice	Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499.	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	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Apparentées	5	6
Résumé	The Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It uses a breadth-first, level-wise search guided by the anti-monotone property of support to efficiently enumerate all item combinations that co-occur above a user-set minimum threshold, then extracts interpretable if-then rules from those patterns.	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.