  In granular computing, fuzzy sets can be approximated by granularly
representable sets that are as close as possible to the original fuzzy set
w.r.t. a given closeness measure. Such sets are called granular approximations.
In this article, we introduce the concepts of disjoint and adjacent granules
and we examine how the new definitions affect the granular approximations.
First, we show that the new concepts are important for binary classification
problems since they help to keep decision regions separated (disjoint granules)
and at the same time to cover as much as possible of the attribute space
(adjacent granules). Later, we consider granular approximations for multi-class
classification problems leading to the definition of a multi-class granular
approximation. Finally, we show how to efficiently calculate multi-class
granular approximations for {\L}ukasiewicz fuzzy connectives. We also provide
graphical illustrations for a better understanding of the introduced concepts.
