Сравнение методов

	Объяснимый FP-Growth	FP-Рост (Рост часто встречаемых паттернов)
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2000 (FP-Growth); XAI augmentation emerged ~2018–present	2000
Автор метода	Han, J., Pei, J., & Yin, Y. (FP-Growth); XAI augmentation from the interpretable ML community	Jiawei Han, Jian Pei & Yiwen Yin
Тип	Explainable frequent pattern mining	Frequent-itemset mining algorithm
Основополагающий источник	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Другие названия	XAI-FP-Growth, interpretable frequent pattern mining, explainable frequent itemset mining, transparent FP-Growth	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Связанные	5	4
Сводка	Explainable FP-Growth augments the classic FP-Growth frequent-pattern mining algorithm with post-hoc interpretability tools — such as rule importance scores, visual pattern trees, and counterfactual explanations — so analysts can not only discover frequent itemsets and association rules but also understand why specific patterns matter, which items drive rule confidence, and how to communicate findings transparently to stakeholders.	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.