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关联规则

关联规则学习是一种无监督技术，用于发现大型事务数据集中共现模式——“如果X则Y”的蕴含关系。它最初由Agrawal、Imielinski和Swami（1993）为超市购物篮分析正式提出，现已广泛应用于电子商务推荐、健康信息学、生物信息学和行为研究。

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Method map

The neighbourhood of related methods — select a node to explore.

关联规则
Apriori算法K-means聚类半监督学习投票集成 (Voting Ensemble)主动学习关联规则贝叶斯关联规则集成关联规则可解释关联规则可解释 FP-Growth在线关联规则

来源

  1. 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 ↗
  2. Tan, P.-N., Steinbach, M., Karpatne, A., & Kumar, V. (2018). Introduction to Data Mining (2nd ed., Ch. 5). Pearson. ISBN: 978-0-13-312890-1

如何引用本页

ScholarGate. (2026, June 3). Association Rule Learning (Market Basket Analysis). ScholarGate. https://scholargate.app/zh/machine-learning/association-rules

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被引用于

主动学习关联规则Apriori算法贝叶斯关联规则集成关联规则可解释关联规则可解释 FP-Growth在线关联规则

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ScholarGate — Association Rules (Association Rule Learning (Market Basket Analysis)). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/association-rules · 数据集： https://doi.org/10.5281/zenodo.20539026