Porovnat metody

	Vysvětlitelný FP-Growth	Algoritmus Apriori
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2000 (FP-Growth); XAI augmentation emerged ~2018–present	1994
Tvůrce	Han, J., Pei, J., & Yin, Y. (FP-Growth); XAI augmentation from the interpretable ML community	Agrawal, R. & Srikant, R.
Typ	Explainable frequent pattern mining	Frequent itemset and association rule mining algorithm
Původní zdroj	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.	Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499.
Další názvy	XAI-FP-Growth, interpretable frequent pattern mining, explainable frequent itemset mining, transparent FP-Growth	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining
Příbuzné	5	5
Shrnutí	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.	The Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It uses a breadth-first, level-wise search guided by the anti-monotone property of support to efficiently enumerate all item combinations that co-occur above a user-set minimum threshold, then extracts interpretable if-then rules from those patterns.