Machine learningMachine learning
贝叶斯关联规则
贝叶斯关联规则通过对规则施加先验概率分布，并根据数据给出的后验概率对规则进行评分，来扩展经典的关联规则挖掘。该贝叶斯框架不依赖于原始支持度和置信度计数进行阈值判断，而是自然地惩罚复杂性，校正多重比较，并为事务型或分类数据集生成校准后的概率规则强度。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3), 197–243. DOI: 10.1007/BF00994016 ↗
- Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 1215, 487–499. link ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Association Rule Mining. ScholarGate. https://scholargate.app/zh/machine-learning/bayesian-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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