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Relative Risk and Odds Ratio: A Data Mining Perspective

Summary: Formulates mining for patterns with high relative risk and odds ratio (prospective vs retrospective), addressing a gap in model-free association discovery. Stratifies pattern space into convex support plateaus and gives sound, complete algorithms to extract most-general/specific patterns as efficiently as frequent-closed mining. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1369
Venue
PODS
Year
2005
Pagerank
5.3372281e-05
Overall Rank
8,993 | 38.31%
DOI
10.1145/1065167.1065215

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{li_pods05,
        address = {New York, NY, USA},
        series = {{PODS} '05},
        title = {{Relative Risk and Odds Ratio: A Data Mining Perspective}},
        url = {https://dl.acm.org/doi/10.1145/1065167.1065215},
        doi = {10.1145/1065167.1065215},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Li, Haiquan and Li, Jinyan and Wong, Limsoon and Feng, Mengling and Tan, Yap-Peng},
        year = {2005}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
1,792 MacroBase: Prioritizing Attention in Fast Data 2017 SIGMOD 9.7436856e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
13 Mining Association Rules between Sets of Items in Large Databases 1993 SIGMOD 0.0006567919
161 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00027981772
886 Efficiently Mining Long Patterns from Databases 1998 SIGMOD 0.0001340645
Previous Page 1 / 1 Next

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