Machine learningPattern mining
FP-Growth (频繁模式增长)
FP-Growth 由 Jiawei Han、Jian Pei 和 Yiwen Yin 于 2000 年提出，它在不生成候选集的情况下从事务数据中挖掘频繁项集，而生成候选集是拖慢经典 Apriori 算法的耗时步骤。它通过两次扫描将数据库压缩成频繁模式树（FP-tree），然后从该结构中递归地增长频繁模式，这使得它在大型、密集型数据集上的速度远超 Apriori。
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来源
- Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI: 10.1145/342009.335372 ↗
- Han, J., Pei, J., Yin, Y., & Mao, R. (2004). Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Discovery, 8(1), 53–87. DOI: 10.1023/B:DAMI.0000005258.31418.83 ↗
如何引用本页
ScholarGate. (2026, June 2). FP-Growth (Frequent Pattern Growth). ScholarGate. https://scholargate.app/zh/machine-learning/fp-growth
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