Machine learningPattern mining
FP-Growth (Frequent Pattern Growth)
FP-Growth, introduced by Jiawei Han, Jian Pei, and Yiwen Yin in 2000, mines frequent itemsets from transaction data without generating candidate sets, the costly step that slows the classic Apriori algorithm. It compresses the database into a frequent-pattern tree (FP-tree) in two scans, then grows frequent patterns recursively from that structure, making it dramatically faster than Apriori on large, dense datasets.
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Sources
- 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 ↗
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Referenced by
Active learning Association rulesApriori AlgorithmBayesian Association RulesECLATEmerging Pattern MiningEnsemble Apriori AlgorithmEnsemble Association RulesExplainable Association RulesExplainable FP-GrowthOnline Association RulesOnline FP-growthSemi-supervised Apriori AlgorithmSemi-supervised Association RulesSemi-supervised FP-growthSequential Pattern Mining