Comparer des méthodes

	FP-croissance semi-supervisé	Arbre de décision	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	2000s–2010s	1984	2001
Auteur d'origine	Extensions of Han, Pei & Yin (2000); semi-supervised variants developed by various authors in the 2000s–2010s	Breiman, Friedman, Olshen & Stone	Breiman, L.
Type	Semi-supervised frequent pattern mining	Recursive partitioning (if-then rules)	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.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	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	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	3	5	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.