Induction and fusion of fuzzy prototypes
In the sequel, we shall be concerned with the automated induction of prototypes to represent a database in a way that combines transparency and accuracy. More precisely, our aim is to automatically summarize the information from a set of data using prototypes and simultaneously decide on the number of prototypes needed to represent the data adequately. We propose to use fuzzy prototypes, which correspond to groupings of similar objects represented by tuples of fuzzy sets over attribute universes. In the case of numerical attributes, the universes will be discretized using linguistic variables and the attribute values will be described by fuzzy sets on words. To learn the prototypes, we shall present an algorithm inspired by hierarchical clustering techniques, which makes use of new measures of prototype similarity and of prototype addition. Also, a pruning algorithm will be incorporated which uses a new heuristic measure of prototype quality to fuse overly specific prototypes. The potential of the resulting method Will be illustrated by its application to a number of classification problems and comparing its performance with that of previous approaches in the literature.
fuzzy prototype hierarchical clustering prototype quality linguistic labels similarity measure