Comparer des méthodes

	Règles d'association par apprentissage actif	Règles d'association semi-supervisées
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2010s	2003–2010s
Auteur d'origine	Dzyuba, V. & van Leeuwen, M.; Boley, M. et al.	Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers)
Type	Interactive pattern mining	Pattern mining with partial supervision
Source fondatrice	Dzyuba, V., & van Leeuwen, M. (2017). Interactive Discovery of Interesting Association Rules by Subjective Interestingness. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Springer.	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.
Alias	interactive association rule mining, active rule mining, query-driven association rule discovery, user-guided association rules	semi-supervised ARM, label-guided association rule mining, constrained association rule mining, semi-supervised pattern discovery
Apparentées	5	4
Résumé	Active learning association rules combines the iterative query-and-label loop of active learning with association rule mining, allowing a human expert to guide the discovery process interactively. Instead of exhaustively enumerating all rules above a fixed support-confidence threshold, the system selects the most informative rule candidates and asks the user to judge their interestingness, focusing the search on subjectively useful patterns.	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.