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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Authors
- 1. Leonardo Pellegrina (University of Padova)
- 2. Fabio Vandin (University of Padova)
BibTeX Citation
@article{pellegrina_vldb24,
title = {{Efficient Discovery of Significant Patterns with Few-Shot Resampling}},
author = {Pellegrina, Leonardo and Vandin, Fabio},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {10},
pages = {2668--2680},
doi = {10.14778/3675034.3675055},
url = {https://doi.org/10.14778/3675034.3675055},
year = {2024}
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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 | 0.0006567919 |
| 4,236 | On Dense Pattern Mining in Graph Streams | 2010 | VLDB | 6.8164649e-05 |
| 4,581 | Mining Graph Patterns Efficiently via Randomized Summaries | 2009 | VLDB | 6.6198548e-05 |
| 5,043 | Efficient Temporal Pattern Mining in Big Time Series Using Mutual Information | 2022 | VLDB | 6.3886615e-05 |
| 5,266 | Fast and Scalable Mining of Time Series Motifs with Probabilistic Guarantees | 2022 | VLDB | 6.2939621e-05 |
| 5,603 | Output Space Sampling for Graph Patterns | 2009 | VLDB | 6.153476e-05 |
| 8,170 | Efficient Discovery of Sequence Outlier Patterns | 2019 | VLDB | 5.4741508e-05 |
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