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	Explainable FP-Growth	FP-Growth (Frequent Pattern Growth)
Ämnesområde	Maskininlärning	Maskininlärning
Familj	Machine learning	Machine learning
Ursprungsår	2000 (FP-Growth); XAI augmentation emerged ~2018–present	2000
Upphovsperson	Han, J., Pei, J., & Yin, Y. (FP-Growth); XAI augmentation from the interpretable ML community	Jiawei Han, Jian Pei & Yiwen Yin
Typ	Explainable frequent pattern mining	Frequent-itemset mining algorithm
Ursprungskälla	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.
Alias	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
Närliggande	5	4
Sammanfattning	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.