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Power-Law Based Estimation of Set Similarity Join Size

Summary: Power-law guided estimation of SSJoin size via compact Min-Hash signatures; exploits frequent signature patterns to count support. A novel lattice-based IE counting method yields linear complexity in lattice size, enabling light-weight mining with high accuracy and efficiency. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10035
Venue
VLDB
Year
2009
Pagerank
6.6972929e-05
Overall Rank
4,450 | 69.48%
DOI
10.14778/1687627.1687702

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{lee_vldb09,
        title = {{Power-Law Based Estimation of Set Similarity Join Size}},
        author = {Lee, Hongrae and Ng, Raymond T. and Shim, Kyuseok},
        journal = {PVLDB},
        series = {{VLDB} '09},
        doi = {10.14778/1687627.1687702},
        url = {https://doi.org/10.14778/1687627.1687702},
        year = {2009}
}

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