Bandingkan kaedah

	Pohon Keputusan	FP-Growth (Pertumbuhan Corak Kerap)
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	1984	2000
Pengasas	Breiman, Friedman, Olshen & Stone	Jiawei Han, Jian Pei & Yiwen Yin
Jenis	Recursive partitioning (if-then rules)	Frequent-itemset mining algorithm
Sumber perintis	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.
Alias	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Berkaitan	5	4
Ringkasan	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.