Work

Few-Shot Resampling for Scalable Statistically-Sound Data Mining

Year: 2026

Type: conference-paper

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.

Source: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

DOI: 10.1145/3770855.3817752

Authors: Leonardo Pellegrina, Fabio Vandin

Institution: University of Padua

Language: English

Topic: Data Mining Algorithms and Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Fondazione Cassa di Risparmio di Padova e Rovigo, Università degli Studi di Padova

OpenAlex ID: W7164586529

FWCI:

Cites:

Cited by:

Related to: