Порівняння методів

	Пояснюваний FP-Growth	Пояснювані правила асоціацій
Галузь	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning
Рік появи	2000 (FP-Growth); XAI augmentation emerged ~2018–present	1993 (rules); 2010s (XAI framing)
Автор методу	Han, J., Pei, J., & Yin, Y. (FP-Growth); XAI augmentation from the interpretable ML community	Agrawal, R., Imielinski, T., & Swami, A. (foundational); XAI framing: broader community (2010s–present)
Тип	Explainable frequent pattern mining	Interpretable pattern mining / XAI technique
Основоположне джерело	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.
Інші назви	XAI-FP-Growth, interpretable frequent pattern mining, explainable frequent itemset mining, transparent FP-Growth	XAI association rules, interpretable association rules, rule-based explanation mining, transparent association rule learning
Пов'язані	5	6
Підсумок	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.	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.