Comparer des méthodes

	Règles d'association semi-supervisées	Algorithme Apriori
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2003–2010s	1994
Auteur d'origine	Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers)	Agrawal, R. & Srikant, R.
Type	Pattern mining with partial supervision	Frequent itemset and association rule mining algorithm
Source fondatrice	Liu, B., Hsu, W., & Ma, Y. (2003). Integrating Classification and Association Rule Mining. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM), pp. 339–346.	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	semi-supervised ARM, label-guided association rule mining, constrained association rule mining, semi-supervised pattern discovery	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining
Apparentées	4	5
Résumé	Semi-supervised association rule mining extends classical association rule learning by incorporating a small amount of labeled data alongside a larger unlabeled dataset. It uses known class information or user-provided constraints to guide the discovery of rules that are both statistically frequent and semantically meaningful, bridging unsupervised pattern mining with light supervision.	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.