Comparer des méthodes

	Règles d'association explicables	Algorithme Apriori
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1993 (rules); 2010s (XAI framing)	1994
Auteur d'origine	Agrawal, R., Imielinski, T., & Swami, A. (foundational); XAI framing: broader community (2010s–present)	Agrawal, R. & Srikant, R.
Type	Interpretable pattern mining / XAI technique	Frequent itemset and association rule mining algorithm
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.	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.
Alias	XAI association rules, interpretable association rules, rule-based explanation mining, transparent association rule learning	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining
Apparentées	6	5
Résumé	Explainable Association Rules leverages the inherently symbolic, if-then structure of association rule mining to provide human-readable explanations of data patterns or black-box model decisions. Because each rule explicitly states its antecedent and consequent together with support, confidence, and lift, the outputs are natively interpretable without requiring a secondary post-hoc surrogate.	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.