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	Explainable Association Rules	FP-Growth (Frequent Pattern Growth)
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	1993 (rules); 2010s (XAI framing)	2000
Twórca	Agrawal, R., Imielinski, T., & Swami, A. (foundational); XAI framing: broader community (2010s–present)	Jiawei Han, Jian Pei & Yiwen Yin
Typ	Interpretable pattern mining / XAI technique	Frequent-itemset mining algorithm
Źródło pierwotne	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.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Inne nazwy	XAI association rules, interpretable association rules, rule-based explanation mining, transparent association rule learning	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Pokrewne	6	4
Podsumowanie	Explainable Association Rules leverages the inherently symbolic, if-then structure of association rule mining to provide human-readable explanations of data patterns or black-box model decisions. Because each rule explicitly states its antecedent and consequent together with support, confidence, and lift, the outputs are natively interpretable without requiring a secondary post-hoc surrogate.	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.