Machine learningMachine learning
可解释 FP-Growth
可解释 FP-Growth 算法在经典的 FP-Growth 频繁模式挖掘算法基础上，增加了事后可解释性工具——例如规则重要性得分、可视化模式树和反事实解释——使分析师不仅能发现频繁项集和关联规则，还能理解特定模式为何重要、哪些项驱动规则置信度，以及如何透明地向利益相关者沟通发现结果。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI: 10.1145/335191.335372 ↗
- Association rule learning. Wikipedia. link ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Frequent Pattern Growth (XAI-Augmented FP-Growth). ScholarGate. https://scholargate.app/zh/machine-learning/explainable-fp-growth
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
- 可解释关联规则机器学习↔ compare
- FP-Growth (频繁模式增长)机器学习↔ compare
- 半监督FP-growth机器学习↔ compare