Under the term behavioral data, we consider any type of data featuring individuals performing observable actions on entities. For instance, voting data depict parliamentarians who express their votes w.r.t. legislative procedures. In this work, we address the problem of discovering exceptional (dis)agreement patterns in such data, i.e., groups of individuals that exhibit an unexpected (dis)agreement under specific contexts compared to what is observed in overall terms. To tackle this problem, we design a generic approach, rooted in the Subgroup Discovery/Exceptional Model Mining framework, which enables the discovery of such patterns in two different ways. A branch-and-bound algorithm ensures an efficient exhaustive search of the underlying search space by leveraging closure operators and optimistic estimates on the interestingness measures. A second algorithm abandons the completeness by using a sampling paradigm which provides an alternative when an exhaustive search approach becomes unfeasible. To illustrate the usefulness of discovering exceptional (dis)agreement patterns, we report a comprehensive experimental study on four real-world datasets relevant to three different application domains: political analysis, rating data analysis and healthcare surveillance.

中文翻译：

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识别小组之间的特殊（分歧）协议

在行为数据一词下，我们考虑以个体对实体执行可观察到的行为为特征的任何类型的数据。例如，投票数据描述了通过立法程序投票的议员。在这项工作中，我们解决了在此类数据中发现异常（不一致）协议模式的问题，即与整体而言相比，在特定背景下表现出意外（不一致）协议的个人群体。为了解决此问题，我们设计了一种通用方法，该方法植根于“子组发现/异常模型挖掘”框架，该框架允许以两种不同方式发现这种模式。分支定界算法通过利用闭包运算符和对趣味性度量的乐观估计，可确保对底层搜索空间进行有效的详尽搜索。第二种算法通过使用采样范式放弃完整性，当穷举搜索方法变得不可行时，采样范式提供了另一种选择。为了说明发现例外（不一致）协议模式的有用性，我们报告了一项针对与三个不同应用领域相关的四个真实世界数据集的全面实验研究：政治分析，评级数据分析和医疗保健监视。