Salīdzināt metodes

	Aktīvās apguves asociācijas likumi	Pusuzraudzīti asociācijas likumi
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2010s	2003–2010s
Autors	Dzyuba, V. & van Leeuwen, M.; Boley, M. et al.	Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers)
Tips	Interactive pattern mining	Pattern mining with partial supervision
Pirmavots	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.
Citi nosaukumi	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
Saistītās	5	4
Kopsavilkums	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.