Comparar métodos

	Árvore de Decisão	FP-Growth (Frequent Pattern Growth)
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1984	2000
Autor original	Breiman, Friedman, Olshen & Stone	Jiawei Han, Jian Pei & Yiwen Yin
Tipo	Recursive partitioning (if-then rules)	Frequent-itemset mining algorithm
Fonte seminal	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Outros nomes	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Relacionados	5	4
Resumo	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.	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.