# Association rules over time

arXiv: https://arxiv.org/abs/2010.03834 · HTML: https://mlsanity.com/paper/2010.03834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [A comprehensive review of visualization methods for association rule mining: Taxonomy, Challenges, Open problems and Future ideas](https://mlsanity.com/paper/2302.12594/) (2023)

## Abstract (arXiv)

Decisions made nowadays by Artificial Intelligence powered systems are usually hard for users to understand. One of the more important issues faced by developers is exposed as how to create more explainable Machine Learning models. In line with this, more explainable techniques need to be developed, where visual explanation also plays a more important role. This technique could also be applied successfully for explaining the results of Association Rule Mining.This Chapter focuses on two issues: (1) How to discover the relevant association rules, and (2) How to express relations between more attributes visually. For the solution of the first issue, the proposed method uses Differential Evolution, while Sankey diagrams are adopted to solve the second one. This method was applied to a transaction database containing data generated by an amateur cyclist in past seasons, using a mobile device worn during the realization of training sessions that is divided into four time periods. The results of visualization showed that a trend in improving performance of an athlete can be indicated by changing the attributes appearing in the selected association rules in different time periods.