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主动学习关联规则

主动学习关联规则将主动学习的迭代查询和标注循环与关联规则挖掘相结合，允许人类专家交互式地指导发现过程。它不是穷举列举所有高于固定支持度-置信度阈值的规则，而是选择信息量最大的候选规则，并要求用户判断其有趣性，从而将搜索重点放在主观上有用的模式上。

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Method map

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

主动学习关联规则
主动学习Apriori算法关联规则FP-Growth (频繁模式增长)半监督关联规则

来源

  1. Dzyuba, V., & van Leeuwen, M. (2017). Interactive Discovery of Interesting Association Rules by Subjective Interestingness. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Springer. link ↗
  2. Boley, M., Lucchese, C., Paurat, D., & Gartner, T. (2013). Direct Local Pattern Sampling by Efficient Two-Step Random Procedures. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 582–590). ACM. link ↗

如何引用本页

ScholarGate. (2026, June 3). Active Learning for Association Rule Mining. ScholarGate. https://scholargate.app/zh/machine-learning/active-learning-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.

Compare side by side →

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