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.

翻译：显著模式挖掘是事务数据挖掘中的一项基础任务，其核心在于识别与给定特征（即目标变量）显著相关的模式。在生物医学、购物篮市场分析及社交网络等多个应用领域中，研究目标通常是发现那些与目标变量之间的关联性需基于潜在总体（或过程）来定义的模式，而现有数据集仅代表该总体的一组观测样本（即抽样数据）。衡量模式与目标变量关联性的自然方法是考察其统计显著性，即评估该模式与目标变量之间关系对独立性（零）假设的偏离程度。尽管已有多种算法被提出用于发现统计显著的模式，但这仍是一项计算密集型任务，且对于子群等复杂模式而言，目前尚无高效解决方案。本文提出FSR算法——一种能够高效识别统计显著模式并严格控制错误发现概率的算法。FSR建立在一种新颖的通用显著模式挖掘框架之上，该框架涵盖了几类最常被考察的模式类型，包括项集、序列模式及子群。FSR通过少量重采样数据集（通过对每个事务独立同分布地分配标签获得）来严格界定衡量模式显著性的质量统计量的上确界偏差。该算法基于新颖的紧致上确界偏差界构建，仅需挖掘少量重采样数据集即可实现，同时在发现显著模式方面具有高效性。以显著子群挖掘作为测试案例，我们在多个真实数据集上的评估表明：FSR在有效发现显著子群的同时，仅需使用极少量的重采样数据集。