Comparer des méthodes

	FP-croissance semi-supervisé	FP-Growth (Frequent Pattern Growth)	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	2000s–2010s	2000	2001
Auteur d'origine	Extensions of Han, Pei & Yin (2000); semi-supervised variants developed by various authors in the 2000s–2010s	Jiawei Han, Jian Pei & Yiwen Yin	Breiman, L.
Type	Semi-supervised frequent pattern mining	Frequent-itemset mining algorithm	Ensemble (bagging of decision trees)
Source fondatrice	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 1–12.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	SS-FP-growth, constrained FP-growth, label-guided frequent pattern mining, semi-supervised frequent itemset mining	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	3	4	4
Résumé	Semi-supervised FP-growth extends the classical Frequent Pattern growth algorithm by incorporating partial labels, user-defined constraints, or class-level information to guide frequent itemset discovery. Instead of mining all patterns indiscriminately, it focuses on patterns that are both statistically frequent and semantically meaningful given the available supervision signal.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.