Few-Shot Resampling for Scalable Statistically-Sound Data Mining
Evaluating statistical significance of data mining results typically requires thousands of resampled datasets, which is impractical for large-scale data. This paper introduces FewRS, a novel resampling approach that needs only an extremely small number of resampled datasets by deriving a new bound on the supremum deviation of test statistics. FewRS provides rigorous guarantees on false discoveries and achieves up to two orders of magnitude speedup on pattern mining and network analysis tasks while maintaining high statistical power.
[2606.11235] Few-Shot Resampling for Scalable Statistically-Sound Data Mining
[Submitted on 29 May 2026]
Title:Few-Shot Resampling for Scalable Statistically-Sound Data Mining
View a PDF of the paper titled Few-Shot Resampling for Scalable Statistically-Sound Data Mining, by Leonardo Pellegrina and Fabio Vandin
View PDF HTML (experimental)
Abstract:A key step in knowledge discovery is the evaluation of data mining results. In several applications, including pattern mining, graph analysis, and others, this step includes the evaluation of the statistical significance of the results, to avoid spurious discoveries due only to noise or random fluctuations in the data. While specialized procedures have been developed for some specific applications, resampling-based approaches are widely used, in particular for complex analyses where analytical results cannot be derived. However, current resampling-based approaches require the generation and analysis of thousands of resampled datasets, and are therefore impractical for large datasets or computationally intensive analyses.
In this paper, we introduce FewRS, a simple and effective resampling-based approach to assess the statistical significance of data mining results with rigorous guarantees on the probability of false discoveries. Our approach can be used in every situation where resampling-based approaches are applied. FewRS builds on our derivation of a novel bound to the supremum deviation of test statistics representing the quality of data mining results. We prove that FewRS needs to generate and analyze an extremely small number of resampled datasets, leading to a highly scalable approach with wide applicability. We test our approach on common tasks such as pattern mining and network analysis. In all cases, our approach results in a reduction of up to two orders of magnitude in running time compared to the state of the art, while preserving high statistical power, enabling the statistical validation of data mining results on large-scale real-world datasets.
Comments: Accepted to KDD 2026
Subjects:
Machine Learning (cs.LG); Databases (cs.DB); Methodology (stat.ME)
Cite as: arXiv:2606.11235 [cs.LG]
(or arXiv:2606.11235v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.11235
arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.1145/3770855.3817752
DOI(s) linking to related resources
Submission history
From: Leonardo Pellegrina [view email] [v1] Fri, 29 May 2026 09:00:26 UTC (3,652 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Few-Shot Resampling for Scalable Statistically-Sound Data Mining, by Leonardo Pellegrina and Fabio Vandin
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.LG
new | recent | 2026-06
Change to browse by:
cs cs.DB stat stat.ME
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)