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 | 0.0010864752 |
| 4,089 | On Dense Pattern Mining in Graph Streams [Extended Abstract] | 2010 | VLDB | 6.4587806e-05 |
| 4,716 | Mining Graph Patterns Efficiently via Randomized Summaries | 2009 | VLDB | 5.9755569e-05 |
| 4,947 | Efficient Temporal Pattern Mining in Big Time Series Using Mutual Information | 2022 | VLDB | 5.8123636e-05 |
| 5,245 | Fast and Scalable Mining of Time Series Motifs with Probabilistic Guarantees | 2022 | VLDB | 5.6067361e-05 |
| 5,436 | Output Space Sampling for Graph Patterns | 2009 | VLDB | 5.5042223e-05 |
| 8,117 | Efficient Discovery of Sequence Outlier Patterns | 2019 | VLDB | 4.5814937e-05 |
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