Сравнение на методи

	Онлайн асоциативни правила	Правила за асоциация
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	1996	1993
Създател	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.	Agrawal, R., Imielinski, T., & Swami, A.
Тип	Incremental / streaming pattern mining	Unsupervised pattern discovery
Основополагащ източник	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.	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.
Други названия	Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM	market basket analysis, association rule mining, frequent itemset mining, affinity analysis
Свързани	5	4
Резюме	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.	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.