Relevance and redundancy in fuzzy classification systems

Fuzzy classification systems is defined in this paper as an
aggregative model, in such a way that Ruspini classical definition
of fuzzy partition appears as a particular case. Once a basic {\em
recursive} model has been accepted, we then propose to analyze
relevance and redundancy in order to allow the possibility of {\em
learning} from previous experiences. All these concepts are
applied to a real picture, showing that our approach allows to
check quality of such a classification system.