Mining Flipping Correlations from Large Datasets with Taxonomies
Summary: Introduces flipping correlations by contrasting correlations across abstraction levels, yielding sign changes under generalization. Flipper outperforms naive mining by orders of magnitude and finds surprising correlations at low-to-medium support. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Marina Barsky
- 2. Sangkyum Kim
- 3. Tim Weninger
- 4. Jiawei Han
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 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 | 0.0010863639 |
| 182 | Mining Frequent Patterns without Candidate Generation | 2000 | SIGMOD | 0.00036955562 |
| 229 | Discovery of Multiple-Level Association Rules from Large Databases | 1995 | VLDB | 0.0003226623 |
| 404 | Mining Generalized Association Rules | 1995 | VLDB | 0.00024159651 |
| 735 | Beyond Market Baskets: Generalizing Association Rules to Correlations | 1997 | SIGMOD | 0.00017417893 |
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