Porovnať metódy

	Semi-supervised FP-growth	Rozhodovací strom	FP-Growth (rast častých vzorov)
Odbor	Strojové učenie	Strojové učenie	Strojové učenie
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	2000s–2010s	1984	2000
Tvorca	Extensions of Han, Pei & Yin (2000); semi-supervised variants developed by various authors in the 2000s–2010s	Breiman, Friedman, Olshen & Stone	Jiawei Han, Jian Pei & Yiwen Yin
Typ	Semi-supervised frequent pattern mining	Recursive partitioning (if-then rules)	Frequent-itemset mining algorithm
Pôvodný zdroj	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.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Ďalšie názvy	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	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Príbuzné	3	5	4
Zhrnutie	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.	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.