Comparar métodos

	FP-Growth Explicável	Regras de Associação Explicáveis
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2000 (FP-Growth); XAI augmentation emerged ~2018–present	1993 (rules); 2010s (XAI framing)
Autor original	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)
Tipo	Explainable frequent pattern mining	Interpretable pattern mining / XAI technique
Fonte seminal	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.
Outros nomes	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
Relacionados	5	6
Resumo	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.