  Opaque models belonging to the machine learning world are ever more exploited
in the most different application areas. These models, acting as black boxes
(BB) from the human perspective, cannot be entirely trusted if the application
is critical unless there exists a method to extract symbolic and human-readable
knowledge out of them. In this paper we analyse a recurrent design adopted by
symbolic knowledge extractors for BB regressors - that is, the creation of
rules associated with hypercubic input space regions. We argue that this kind
of partitioning may lead to suboptimal solutions when the data set at hand is
high-dimensional or does not satisfy symmetric constraints. We then propose a
(deep) clustering-based approach to be performed before symbolic knowledge
extraction to achieve better performance with data sets of any kind.
