Efficient Discovery of Significant Patterns with Few-Shot Resampling
Leonardo Pellegrina, Fabio Vandin
TL;DR
The work tackles significant pattern mining under multiple hypotheses by introducing FSR, a few-shot resampling framework that provides rigorous $FWER$ guarantees while efficiently handling complex pattern languages (e.g., itemsets, subgroups, sequences). It presents a unified approach for both conditional and unconditional testing, underpinned by new theoretical bounds that relate resampled and true pattern qualities and require only a small number of resamples. Empirically, FSR achieves high power with modest computational cost, outperforming permutation-based baselines for conditional testing and offering a practical unconditional alternative, demonstrated on large real datasets and in neural-network interpretation tasks. The method promises broad applicability to diverse pattern types, enabling scalable, reliable discovery of significant patterns in big data contexts.
Abstract
Significant pattern mining is a fundamental task in mining transactional data, requiring to identify patterns significantly associated with the value of a given feature, the target. In several applications, such as biomedicine, basket market analysis, and social networks, the goal is to discover patterns whose association with the target is defined with respect to an underlying population, or process, of which the dataset represents only a collection of observations, or samples. A natural way to capture the association of a pattern with the target is to consider its statistical significance, assessing its deviation from the (null) hypothesis of independence between the pattern and the target. While several algorithms have been proposed to find statistically significant patterns, it remains a computationally demanding task, and for complex patterns such as subgroups, no efficient solution exists. We present FSR, an efficient algorithm to identify statistically significant patterns with rigorous guarantees on the probability of false discoveries. FSR builds on a novel general framework for mining significant patterns that captures some of the most commonly considered patterns, including itemsets, sequential patterns, and subgroups. FSR uses a small number of resampled datasets, obtained by assigning i.i.d. labels to each transaction, to rigorously bound the supremum deviation of a quality statistic measuring the significance of patterns. FSR builds on novel tight bounds on the supremum deviation that require to mine a small number of resampled datasets, while providing a high effectiveness in discovering significant patterns. As a test case, we consider significant subgroup mining, and our evaluation on several real datasets shows that FSR is effective in discovering significant subgroups, while requiring a small number of resampled datasets.