(FARs) to users in a graph format. Association rules identify and represent dependencies between data items in a dataset. Those dependencies are graphically depicted through fuzzy inference-grams, or *fingrams*, which are networks where nodes represent fuzzy rules and the weighted edges represent interactions between rules. Nodes are always labeled with relevant textual information and the edge weighting naturally leads to the formation of distinguishable groups of rules, each associated with some value for a given variable. Although this graphical representation clearly facilitates the visual analysis and comprehension of fuzzy association rules [@pancho2013interpretability], no evaluation was carried out, and the assessment of its usefulness through user feedback remains to be done.

Going beyond the theoretical aspects explored by most of the research in the area, Riid et al. [-@riid2013determination] proposed a practical application to take advantage of an interpretable fuzzy model. They employ a fuzzy classifier to explain the geographical variation of Estonian folk songs metrical features. Hierarchical Clustering (HC), an agglomerative procedure based on the idea that objects tend to be more related to nearby objects than to objects farther away, is used to determine the cluster of geographical regions that show similarities regarding the verse metre. A data-driven fuzzy 