Efficient Discovery of Significant Patterns with Few-Shot Resampling
Summary: FSR: efficient significant-pattern mining using few i.i.d. label resamples to tightly bound the supremum deviation of quality scores, yielding rigorous false-discovery control. Framework covers itemsets, sequences and subgroups; finds significant subgroups with far fewer resamples than prior work. (summarized by gpt-5-mini on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 13 | Mining Association Rules between Sets of Items in Large Databases | 1993 | SIGMOD | 0.0010863639 |
| 4,092 | On Dense Pattern Mining in Graph Streams [Extended Abstract] | 2010 | VLDB | 6.4525563e-05 |
| 4,713 | Mining Graph Patterns Efficiently via Randomized Summaries | 2009 | VLDB | 5.9694403e-05 |
| 4,951 | Efficient Temporal Pattern Mining in Big Time Series Using Mutual Information | 2022 | VLDB | 5.8067758e-05 |
| 5,248 | Fast and Scalable Mining of Time Series Motifs with Probabilistic Guarantees | 2022 | VLDB | 5.6013482e-05 |
| 5,447 | Output Space Sampling for Graph Patterns | 2009 | VLDB | 5.498563e-05 |
| 8,122 | Efficient Discovery of Sequence Outlier Patterns | 2019 | VLDB | 4.5771031e-05 |
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