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.

本文通过进行系统化文献综述，提供了从 1996 年到 2022 年发表的 1140 篇学术论文的方法、算法、指标和数据集的深入研究，探讨了数字联想规则挖掘的重要研究问题、现状和未来可能性，并提出了一种新颖的离散化度量来提供与人类分区感知相符的数字数据分区。