So sánh phương pháp

	Luật kết hợp có thể giải thích	Cây quyết định giải thích được
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1993 (rules); 2010s (XAI framing)	1984 (CART); XAI framing formalized 2010s–2020s
Người khởi xướng	Agrawal, R., Imielinski, T., & Swami, A. (foundational); XAI framing: broader community (2010s–present)	Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.
Loại	Interpretable pattern mining / XAI technique	Interpretable supervised learning model
Công trình gốc	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.	Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole. ISBN: 978-0-412-04841-8
Tên gọi khác	XAI association rules, interpretable association rules, rule-based explanation mining, transparent association rule learning	XDT, interpretable decision tree, rule-based decision tree, transparent decision tree
Liên quan	6	4
Tóm tắt	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.	An Explainable Decision Tree is a classification or regression tree deliberately grown to be shallow, readable, and auditable — producing a finite set of if-then rules that a human can verify without additional tools. It sits at the intersection of predictive modelling and Explainable AI (XAI), chosen when stakeholders must understand and trust every prediction the model makes.