Сравнение методов

	Онлайн-ассоциативные правила	Алгоритм Apriori
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1996	1994
Автор метода	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.	Agrawal, R. & Srikant, R.
Тип	Incremental / streaming pattern mining	Frequent itemset and association rule mining algorithm
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.
Другие названия	Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining
Связанные	5	5
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.