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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
1370
Venue
PODS
Year
2005
Pagerank
4.4576449e-05
Overall Rank
8,732 | 39.26%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
2,126 MacroBase: Prioritizing Attention in Fast Data 2017 SIGMOD 9.4887794e-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 0.0010864752
181 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00036992674
840 Efficiently Mining Long Patterns from Databases 1998 SIGMOD 0.00016058396
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