방법 비교

	온라인 연관 규칙	Apriori 알고리즘	FP-성장 (빈발 패턴 성장)
분야	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	1996	1994	2000
창시자	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.	Agrawal, R. & Srikant, R.	Jiawei Han, Jian Pei & Yiwen Yin
유형	Incremental / streaming pattern mining	Frequent itemset and association rule mining algorithm	Frequent-itemset mining algorithm
원전	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.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
별칭	Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
관련	5	5	4
요약	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.	FP-Growth, introduced by Jiawei Han, Jian Pei, and Yiwen Yin in 2000, mines frequent itemsets from transaction data without generating candidate sets, the costly step that slows the classic Apriori algorithm. It compresses the database into a frequent-pattern tree (FP-tree) in two scans, then grows frequent patterns recursively from that structure, making it dramatically faster than Apriori on large, dense datasets.