Сравнение методов

	Активное обучение для поиска правил ассоциаций	Правила ассоциаций
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2010s	1993
Автор метода	Dzyuba, V. & van Leeuwen, M.; Boley, M. et al.	Agrawal, R., Imielinski, T., & Swami, A.
Тип	Interactive pattern mining	Unsupervised pattern discovery
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.	Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216.
Другие названия	interactive association rule mining, active rule mining, query-driven association rule discovery, user-guided association rules	market basket analysis, association rule mining, frequent itemset mining, affinity analysis
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.	Association rule learning is an unsupervised technique that discovers co-occurrence patterns — 'if X then Y' implications — within large transactional datasets. Originally formalized by Agrawal, Imielinski, and Swami (1993) for supermarket basket analysis, it is now widely applied in e-commerce recommendation, health informatics, bioinformatics, and behavioral research.