Controlling False Positives in Association Rule Mining
Summary: Controls false positives in association-rule mining by applying three multiple-testing corrections: direct adjustment, permutation-based, and holdout. Finds many spurious rules without correction; permutation-based approach offers strongest power but is expensive, with cost-reduction techniques proposed. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Guimei Liu
- 2. Haojun Zhang
- 3. Limsoon Wong
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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 |
| 744 | Beyond Market Baskets: Generalizing Association Rules to Correlations | 1997 | SIGMOD | 0.00017333019 |
| 4,829 | An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets | 2009 | PODS | 5.8925807e-05 |
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