Machine learningMachine learning
集成关联规则
集成关联规则将集成学习原理应用于关联规则挖掘：从不同的数据子样本或使用不同的参数发现多个规则集，然后合并和加权以生成更稳定、更完整的共现模式集。该方法降低了对支持度和置信度阈值选择的敏感性，并提高了在有噪声的事务数据上的鲁棒性。
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
- Domingos, P. (1999). MetaCost: A general method for making classifiers cost-sensitive. Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 155–164. link ↗
- Rymon, R. (1992). Search through systematic set enumeration. Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning, 539–550. — foundational work on systematic enumeration used in ensemble aggregation of frequent itemsets. link ↗
如何引用本页
ScholarGate. (2026, June 3). Ensemble Association Rule Mining. ScholarGate. https://scholargate.app/zh/machine-learning/ensemble-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.
- Apriori算法机器学习↔ compare
- 关联规则机器学习↔ compare
- Bagging（Bootstrap Aggregating）机器学习↔ compare
- Boosting机器学习↔ compare
- FP-Growth (频繁模式增长)机器学习↔ compare
- 投票集成 (Voting Ensemble)机器学习↔ compare