방법 비교

	준지도학습 FP-growth	결정 트리
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2000s–2010s	1984
창시자	Extensions of Han, Pei & Yin (2000); semi-supervised variants developed by various authors in the 2000s–2010s	Breiman, Friedman, Olshen & Stone
유형	Semi-supervised frequent pattern mining	Recursive partitioning (if-then rules)
원전	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.
별칭	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
관련	3	5
요약	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.