Comparer des méthodes

	Algorithme Apriori	Règles d'association
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1994	1993
Auteur d'origine	Agrawal, R. & Srikant, R.	Agrawal, R., Imielinski, T., & Swami, A.
Type	Frequent itemset and association rule mining algorithm	Unsupervised pattern discovery
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.	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.
Alias	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	market basket analysis, association rule mining, frequent itemset mining, affinity analysis
Apparentées	5	4
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.	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.