Machine learningMachine learning
可解释关联规则
可解释关联规则利用关联规则挖掘固有的符号化（即“如果-那么”）结构，来提供人类可读的数据模式或黑箱模型决策的解释。由于每条规则都明确陈述了其前提和结论，以及支持度、置信度和提升度，因此其输出本身就具有可解释性，无需进行二次事后代理。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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. DOI: 10.1145/170035.170072 ↗
- Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44), 22071–22080. DOI: 10.1073/pnas.1900654116 ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Association Rules Mining. ScholarGate. https://scholargate.app/zh/machine-learning/explainable-association-rules
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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