Clustering task is a never-ending research topic. New methods are permanently proposed. In particular, Fuzzy Logic and Self-organizing Maps and their mutual cooperation have demonstrated to be interesting paradigms. We propose a general approach to obtain membership functions for a ranked clustering system based on fuzzy predicates logical operations, considering Gaussian-shaped curves. We find membership functions parameters from trained Self-organizing Maps, which generalize the statistical characteristics of data. The system is self-configured and it has the advantages of other fuzzy ap- proaches. Clustering quality is assessed by labeled data, which allow computing accuracy. The proposal must be tested with more real datasets, though the pre- liminary results obtained in well-known datasets suggest that it is a promising clustering scheme.
['Gustavo Meschino', 'Diego S. Comas', 'Virginia L. Ballarin', 'Adriana Scandurra', 'Lucía Isabel Passoni']
Using SOM as a Tool for Automated Design of Clustering Systems Based on Fuzzy Predicates