We present MiSoSouP, a suite of algorithms for extracting high-quality approximations of the most interesting subgroups, according to different popular interestingness measures, from a random sample of a transactional dataset. We describe a new formulation of these measures as functions of averages, that makes it possible to approximate them using sampling. We then discuss how pseudodimension, a key concept from statistical learning theory, relates to the sample size needed to obtain an high-quality approximation of the most interesting subgroups. We prove an upper bound on the pseudodimension of the problem at hand, which depends on characteristic quantities of the dataset and of the language of patterns of interest. This upper bound then leads to small sample sizes. Our evaluation on real datasets shows that MiSoSouP outperforms state-of-the-art algorithms offering the same guarantees, and it vastly speeds up the discovery of subgroups w.r.t. analyzing the whole dataset.

中文翻译：

---

味噌汤

我们提出了 MiSoSouP，这是一套算法，用于根据不同的流行兴趣度量，从事务数据集的随机样本中提取最有趣子组的高质量近似值。我们将这些度量的新公式描述为平均值的函数，这使得使用采样来近似它们成为可能。然后，我们讨论了伪维度（统计学习理论中的一个关键概念）如何与获得最有趣子组的高质量近似所需的样本量相关。我们证明了手头问题的伪维度的上限，这取决于数据集的特征量和感兴趣模式的语言。这个上限会导致样本量较小。