Linganisha mbinu

	Sheria za Muungano za Kujifunza kwa Vitendo	Sheria za Chama cha Semi-zilizosimamiwa
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2010s	2003–2010s
Mwanzilishi	Dzyuba, V. & van Leeuwen, M.; Boley, M. et al.	Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers)
Aina	Interactive pattern mining	Pattern mining with partial supervision
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	5	4
Muhtasari	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.