Salīdzināt metodes

	Skaidrojamie asociācijas likumi	FP-Growth (biežo kopu augšana)
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1993 (rules); 2010s (XAI framing)	2000
Autors	Agrawal, R., Imielinski, T., & Swami, A. (foundational); XAI framing: broader community (2010s–present)	Jiawei Han, Jian Pei & Yiwen Yin
Tips	Interpretable pattern mining / XAI technique	Frequent-itemset mining algorithm
Pirmavots	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.
Citi nosaukumi	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
Saistītās	6	4
Kopsavilkums	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.