方法对比

	在线关联规则	FP-Growth (频繁模式增长)
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	1996	2000
提出者	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.	Jiawei Han, Jian Pei & Yiwen Yin
类型	Incremental / streaming pattern mining	Frequent-itemset mining algorithm
开创性文献	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.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
别名	Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
相关	5	4
摘要	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.	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.