Association rules over time
This work addresses the need for more explainable AI techniques, specifically for association rule mining in domains like sports analytics, though it is incremental in nature.
The paper tackled the problem of making association rule mining results more explainable by developing a method that uses Differential Evolution to discover relevant rules and Sankey diagrams for visual representation, applied to a cyclist's training data across four time periods to show trends in performance improvement.
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.