Сравнение методов

	Активное обучение для поиска правил ассоциаций	Полуавтоматическое извлечение правил ассоциаций
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2010s	2003–2010s
Автор метода	Dzyuba, V. & van Leeuwen, M.; Boley, M. et al.	Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers)
Тип	Interactive pattern mining	Pattern mining with partial supervision
Основополагающий источник	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.
Другие названия	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
Связанные	5	4
Сводка	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.