Comparer des méthodes

	FP-Growth (Frequent Pattern Growth)	Apprentissage en ligne
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000	1958–2000s
Auteur d'origine	Jiawei Han, Jian Pei & Yiwen Yin	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Type	Frequent-itemset mining algorithm	Learning paradigm (sequential model update)
Source fondatrice	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Alias	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme	incremental learning, sequential learning, streaming learning, online machine learning
Apparentées	4	6
Résumé	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.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.