Сравнение методов

	Онлайн-ассоциативные правила	Алгоритм Apriori	Правила ассоциаций
Область	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	1996	1994	1993
Автор метода	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.	Agrawal, R. & Srikant, R.	Agrawal, R., Imielinski, T., & Swami, A.
Тип	Incremental / streaming pattern mining	Frequent itemset and association rule mining algorithm	Unsupervised pattern discovery
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.	Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499.	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	Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	market basket analysis, association rule mining, frequent itemset mining, affinity analysis
Связанные	5	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.	The Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It uses a breadth-first, level-wise search guided by the anti-monotone property of support to efficiently enumerate all item combinations that co-occur above a user-set minimum threshold, then extracts interpretable if-then rules from those 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.