手法を比較

	オンライン相関ルールマイニング	Aprioriアルゴリズム	アソシエーションルール
分野	機械学習	機械学習	機械学習
系統	Machine learning	Machine learning	Machine learning
提唱年	1996	1994	1993
提唱者	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.	Agrawal, R. & Srikant, R.	Agrawal, R., Imielinski, T., & Swami, A.
種類	Incremental / streaming pattern mining	Frequent itemset and association rule mining algorithm	Unsupervised pattern discovery
原典	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.
別名	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
関連	5	5	4
概要	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.