et al. [-@balazs2013stochastic] proposed an approach based on meaning preservation (MP) – having a common vocabulary with the user, by using linguistic terms in the same sense as the user employs them – and a parameterizable search space narrowing method aimed at adjusting the trade-off between interpretability and accuracy commonly observed in fuzzy systems. Interpretability is not measured, but rather regarded as a binary feature: if the resulting rule base meets the predefined interpretability conditions then it is referred to as a valid interpretable solution.

Mencar et al. [-@mencar2013design] argue that the fulfillment of many interpretability constraints (distinguishability, coverage, special elements, etc. [@mencar2008interpretability]) is guaranteed if Strong Fuzzy Partitions (SFPs) are adopted. A *fuzzy partition* of the data feature is the collection of the fuzzy sets associated with each linguistic term in the model. The authors propose an approach for defining interpretable SFPs based on *cuts*, points of separation between clusters within the data. Again, interpretability is not measured but considered as a natural outcome of the fuzzy partition-based adopted modeling technique.

Visual representation of rules is another resource employed to allow the interpretation of a fuzzy model. Pancho et al. [-@pancho2013interpretability] describe a technique to present fuzzy association rules 