Сравнение методов

	Полуавтоматический FP-growth	FP-Рост (Рост часто встречаемых паттернов)
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2000s–2010s	2000
Автор метода	Extensions of Han, Pei & Yin (2000); semi-supervised variants developed by various authors in the 2000s–2010s	Jiawei Han, Jian Pei & Yiwen Yin
Тип	Semi-supervised frequent pattern mining	Frequent-itemset mining algorithm
Основополагающий источник	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.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Другие названия	SS-FP-growth, constrained FP-growth, label-guided frequent pattern mining, semi-supervised frequent itemset mining	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Связанные	3	4
Сводка	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.	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.