Information granulation and resulting information granules themselves become an important design aspect of fuzzy models.
The formal framework of information granulation and resulting information granules themselves become an important design facet of the fuzzy models.
The idea of information granulation inherently arises in the design process of Sugeno-type fuzzy model, whereas information granulation is closely related with the developed information granules.
Information granules of this type are called interpretable.
For illustrative purposes, the study is focused on information granules formalized in terms of intervals (however the proposed approach becomes equally relevant for other formalism of information granules).
Furthermore, granularity, complexity, and accuracy are associated with fuzzy information granules.
The design of information granules is central to the discipline of Granular Computing.
In this paper, we deal with a particular type of information granules, namely interval-valued data.
We discuss several main approaches to the design of information granules.
Then the induced information granules are used to approximate the decision with lower and upper approximations.
In this study, we address a fundamental issue of the design of "meaningful" information granules.
