Machine learningPattern mining
关联规则挖掘（Apriori）
关联规则挖掘是一种无监督数据挖掘技术，用于发现交易型数据集中项之间的共现模式。它由Agrawal、Imieliński和Swami于1993年正式提出，并由Agrawal和Srikant于1994年通过里程碑式的Apriori算法进行改进，识别形式为X ⇒ Y的规则——表示包含项集X的交易也倾向于包含项集Y——通过支持度、置信度和提升度进行量化。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216. DOI: 10.1145/170035.170072 ↗
- Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference, 487–499. link ↗
如何引用本页
ScholarGate. (2026, June 2). Association Rule Mining (Apriori). ScholarGate. https://scholargate.app/zh/machine-learning/association-rule-mining
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.
Compare side by side →