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)
Incoming Non-self Citations Over Time
Authors
- 1. Haiquan Li
- 2. Jinyan Li
- 3. Limsoon Wong
- 4. Mengling Feng
- 5. Yap-Peng Tan
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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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,623 | Exploratory Mining via Constrained Frequent Set Queries | 1999 | SIGMOD | 4.989399e-05 |
| 13,889 | Towards Data Mining Benchmarking: A Test Bed for Performance Study of Frequent Pattern Mining | 2000 | SIGMOD | - |
| 599 | Mining Quantitative Association Rules in Large Relational Tables | 1996 | SIGMOD | 0.00019394214 |
| 14,020 | Data Mining Techniques | 1996 | SIGMOD | - |
| 11,039 | Efficient Discovery of Significant Patterns with Few-Shot Resampling | 2024 | VLDB | 4.1945683e-05 |
| 3,822 | Association Rules over Interval Data | 1997 | SIGMOD | 6.7263391e-05 |
| 4,643 | Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases | 1998 | VLDB | 6.0261932e-05 |
| 4,398 | Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization | 1996 | SIGMOD | 6.2225159e-05 |
| 4,218 | Mining Optimized Association Rules for Numeric Attributes | 1996 | PODS | 6.3511866e-05 |
| 6,805 | Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining | 1998 | VLDB | 4.9222308e-05 |