  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.
