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: hdcea811788a3efd6
Venue: PODS
Year: 2005
Pagerank: 5.2178616e-05
Overall Rank: 9,151 | 38.48%
DOI: 10.1145/1065167.1065215

Incoming Non-self Citations Over Time

Authors

1. Haiquan Li (Institute for Infocomm Research)
2. Jinyan Li (Institute for Infocomm Research)
3. Limsoon Wong (Institute for Infocomm Research)
4. Mengling Feng (Nanyang Technological University)
5. Yap-Peng Tan (Nanyang Technological University)

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
1,833	MacroBase: Prioritizing Attention in Fast Data	2017	SIGMOD	9.5405247e-05

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	0.00064420972
164	Mining Frequent Patterns without Candidate Generation	2000	SIGMOD	0.00027412227
912	Efficiently Mining Long Patterns from Databases	1998	SIGMOD	0.00013115246

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