使用MDL发现数字目标的未完成子组列表
Discovering outstanding subgroup lists for numeric targets using MDL
摘要：
子组发现（SD）的任务是找到一个数据集的子集，突出相对于目标属性的可解释的描述。为了解决挖掘大量冗余子群的问题，已经提出了子群集发现（SSD）。然而，最先进的SSD方法有其局限性，因为它们通常严重依赖于几何学和/或用户选择的超参数。我们提出了一个分散意识的问题制定的子群集发现，是基于最小描述长度（MDL）的原则和子群列表。我们认为，最好的子组列表是一个最好的总结数据给定的目标的总体分布。我们限制我们的重点是一个单一的数字目标变量，并表明，我们的形式化符合现有的质量措施时，找到一个单一的子群，但除此之外，它允许权衡子群质量与子群的复杂性。接下来，我们提出SSD++，一个启发式算法，我们的经验表明，它返回优秀的子组列表：非冗余集的紧凑的子组，突出具有强烈的偏离手段和小的蔓延。
英文摘要：
The task of subgroup discovery (SD) is to find interpretable descriptions of subsets of a dataset that stand out with respect to a target attribute. To address the problem of mining large numbers of redundant subgroups, subgroup set discovery (SSD) has been proposed. State-of-the-art SSD methods have their limitations though, as they typically heavily rely on heuristics and/or user-chosen hyperparameters. We propose a dispersion-aware problem formulation for subgroup set discovery that is based on the minimum description length (MDL) principle and subgroup lists. We argue that the best subgroup list is the one that best summarizes the data given the overall distribution of the target. We restrict our focus to a single numeric target variable and show that our formalization coincides with an existing quality measure when finding a single subgroup, but that-in addition-it allows to trade off subgroup quality with the complexity of the subgroup. We next propose SSD++, a heuristic algorithm for which we empirically demonstrate that it returns outstanding subgroup lists: non-redundant sets of compact subgroups that stand out by having strongly deviating means and small spread.
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