Comparer des méthodes

	Boosting	FP-Growth (Frequent Pattern Growth)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1990–1997	2000
Auteur d'origine	Schapire, R. E.; Freund, Y.	Jiawei Han, Jian Pei & Yiwen Yin
Type	Sequential ensemble (iterative reweighting)	Frequent-itemset mining algorithm
Source fondatrice	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Alias	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Apparentées	6	4
Résumé	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.