Сравнение методов

	Онлайн-ассоциативные правила	Полуавтоматическое извлечение правил ассоциаций
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1996	2003–2010s
Автор метода	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.	Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers)
Тип	Incremental / streaming pattern mining	Pattern mining with partial supervision
Основополагающий источник	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y. (1996). Maintenance of discovered association rules in large databases: an incremental updating technique. In Proceedings of the 12th International Conference on Data Engineering (ICDE 1996), pp. 106–114. IEEE.	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	Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM	semi-supervised ARM, label-guided association rule mining, constrained association rule mining, semi-supervised pattern discovery
Связанные	5	4
Сводка	Online association rule mining discovers if-then patterns (e.g., buying bread implies buying butter) from transactional data that arrives incrementally or as a stream, updating existing rules and item counts without re-scanning the entire historical database each time new records arrive.	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.