方法对比

	可解释 FP-Growth	关联规则
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2000 (FP-Growth); XAI augmentation emerged ~2018–present	1993
提出者	Han, J., Pei, J., & Yin, Y. (FP-Growth); XAI augmentation from the interpretable ML community	Agrawal, R., Imielinski, T., & Swami, A.
类型	Explainable frequent pattern mining	Unsupervised pattern discovery
开创性文献	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.	Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216.
别名	XAI-FP-Growth, interpretable frequent pattern mining, explainable frequent itemset mining, transparent FP-Growth	market basket analysis, association rule mining, frequent itemset mining, affinity analysis
相关	5	4
摘要	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.	Association rule learning is an unsupervised technique that discovers co-occurrence patterns — 'if X then Y' implications — within large transactional datasets. Originally formalized by Agrawal, Imielinski, and Swami (1993) for supermarket basket analysis, it is now widely applied in e-commerce recommendation, health informatics, bioinformatics, and behavioral research.