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Mining Frequent Patterns without Candidate Generation

Summary: Proposes FP-tree, a compact prefix-tree that compresses frequent-pattern data and eliminates candidate generation. FP-growth mines all patterns via pattern fragment growth and divide-and-conquer on conditional databases, cutting scans and outperforming Apriori by roughly tenfold. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3230
Venue
SIGMOD
Year
2000
Pagerank
0.00027981772
Overall Rank
161 | 98.90%
DOI
10.1145/342009.335372

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{han_sigmod00,
        title = {{Mining Frequent Patterns without Candidate Generation}},
        author = {Han, Jiawei and Pei, Jian and Yin, Yiwen},
        series = {{SIGMOD} '00},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/342009.335372},
        url = {https://dl.acm.org/doi/10.1145/342009.335372},
        year = {2000}
}

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