# Numerical Association Rule Mining: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2307.00662 · HTML: https://mlsanity.com/paper/2307.00662/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 116 references that are in the map.
- First posted on arXiv 2 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [A comprehensive review of visualization methods for association rule mining: Taxonomy, Challenges, Open problems and Future ideas](https://mlsanity.com/paper/2302.12594/) (2023)

## Built on by (most cited first, 6 of 6)

- [Neurosymbolic Association Rule Mining from Tabular Data](https://mlsanity.com/paper/2504.19354/) (2025)
- [Semantic Association Rule Learning from Time Series Data and Knowledge Graphs](https://mlsanity.com/paper/2310.07348/) (2023)
- [Learning Semantic Association Rules from Internet of Things Data](https://mlsanity.com/paper/2412.03417/) (2024)
- [Discovering Association Rules in High-Dimensional Small Tabular Data](https://mlsanity.com/paper/2509.20113/) (2025)
- [Tabular Foundation Models Can Learn Association Rules](https://mlsanity.com/paper/2602.14622/) (2026)
- [AE SemRL: Learning Semantic Association Rules with Autoencoders](https://mlsanity.com/paper/2403.18133/) (2024)

## Abstract (arXiv)

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.