Numerical association rule mining is a widely used variant of the association rule mining technique, and it has been extensively used in discovering patterns and relationships in numerical data. Initially, researchers and scientists integrated numerical attributes in association rule mining using various discretization approaches; however, over time, a plethora of alternative methods have emerged in this field. Unfortunately, the increase of alternative methods has resulted into a significant knowledge gap in understanding diverse techniques employed in numerical association rule mining -- this paper attempts to bridge this knowledge gap by conducting a comprehensive systematic literature review. We provide an in-depth study of diverse methods, algorithms, metrics, and datasets derived from 1,140 scholarly articles published from the inception of numerical association rule mining in the year 1996 to 2022. In compliance with the inclusion, exclusion, and quality evaluation criteria, 68 papers were chosen to be extensively evaluated. To the best of our knowledge, this systematic literature review is the first of its kind to provide an exhaustive analysis of the current literature and previous surveys on numerical association rule mining. The paper discusses important research issues, the current status, and future possibilities of numerical association rule mining. On the basis of this systematic review, the article also presents a novel discretization measure that contributes by providing a partitioning of numerical data that meets well human perception of partitions.

翻译：数值关联规则挖掘是关联规则挖掘技术的一种广泛使用的变体，已被大量应用于发现数值数据中的模式和关系。最初，研究人员和科学家通过多种离散化方法将数值属性整合到关联规则挖掘中；然而，随着时间的推移，该领域涌现了大量替代方法。遗憾的是，替代方法的增加导致了理解数值关联规则挖掘中各种技术的重要知识鸿沟——本文通过开展一项全面的系统性文献综述，试图弥合这一知识鸿沟。我们对来自1996年数值关联规则挖掘诞生至2022年间发表的1140篇学术论文中的多种方法、算法、指标和数据集进行了深入研究。根据纳入、排除和质量评估标准，最终选定68篇论文进行详尽评估。据我们所知，本系统性文献综述首次对当前文献及以往关于数值关联规则挖掘的综述进行了全面分析。本文讨论了数值关联规则挖掘的重要研究问题、当前现状及未来可能性。基于此项系统性综述，文章还提出了一种新型离散化度量，该度量通过提供符合人类分区感知的数值数据划分做出了贡献。