Detecting sets of relevant patterns from a given dataset is an important challenge in data mining. The relevance of a pattern, also called utility in the literature, is a subjective measure and can be actually assessed from very different points of view. Rule-based languages like Answer Set Programming (ASP) seem well suited for specifying user-provided criteria to assess pattern utility in a form of constraints; moreover, declarativity of ASP allows for a very easy switch between several criteria in order to analyze the dataset from different points of view. In this paper, we make steps toward extending the notion of High Utility Pattern Mining (HUPM); in particular we introduce a new framework that allows for new classes of utility criteria not considered in the previous literature. We also show how recent extensions of ASP with external functions can support a fast and effective encoding and testing of the new framework. To demonstrate the potential of the proposed framework, we exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients. Finally, an extensive experimental activity demonstrates both from a quantitative and a qualitative point of view the effectiveness of the proposed approach. Under consideration in Theory and Practice of Logic Programming (TPLP)

翻译：从给定数据集中检测相关模式集是数据挖掘中的重要挑战。模式的相关性（文献中也称为效用）是一种主观度量，可从截然不同的角度进行评估。基于规则的编程语言（如回答集编程，ASP）似乎非常适合以约束形式指定用户提供的标准来评估模式效用；此外，ASP的声明性使得在不同标准间轻松切换成为可能，从而从不同角度分析数据集。本文致力于扩展高效用模式挖掘（HUPM）的概念；特别地，我们引入了一个新框架，允许考虑先前文献中未涉及的新类别效用标准。我们还展示了如何利用ASP在外部函数方面的最新扩展，以支持新框架的快速高效编码与测试。为证明所提出框架的潜力，我们将其作为构建模块，用于定义预测COVID-19患者入住ICU的创新方法。最后，广泛的实验活动从定量和定性两个角度证明了所提出方法的有效性。本文正在《逻辑编程理论与实践》（TPLP）审稿中。