Comparer des méthodes

	Règles d'association en ligne	FP-Growth (Frequent Pattern Growth)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996	2000
Auteur d'origine	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.	Jiawei Han, Jian Pei & Yiwen Yin
Type	Incremental / streaming pattern mining	Frequent-itemset mining algorithm
Source fondatrice	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.
Alias	Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Apparentées	5	4
Résumé	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.