• RELATIONAL CLUSTERING
• RADIAL BASIS FUNCTION (RBF)
• NOISE PROTOTIPE
• NEURAL RECEPTIVE FIELDS
• NEURAL NETWORKS
• NETWORKS
• GENETIC ALGORITHMS
• FUZZY INTERPRETABILITY
• FUZZY IDENTIFICATION
• FUZZY CLUSTERING
• CONDITIONAL CLUSTERING
• CLUSTER VALIDITY
• ROBUST FUZZY RELATIONAL CLUSTERING
• SIMILARITY LEARNING
• SIMILARITY RELATION
• TAKAGI-SUGENO MODEL

ONE OF THE CRITICAL ASPECTS OF CLUSTERING ALGORITHMS IS THE CORRECT IDENTIFICATION OF THE DISSIMILARITY MEASURE USED TO DRIVE THE PARTITIONING OF THE DATA SET. THE DISSIMILARITY MEASURE INDUCES THE CLUSTER SHAPE AND THEREFORE DETERMINES THE SUCCESS OF CLUSTERING ALGORITHMS. AS CLUSTER SHAPES CHANGE FROM A DATA SET TO ANOTHER, DISSIMILARITY MEASURES SHOULD BE EXTRACTED FROM DATA. TO THIS AIM, WE EXPLOIT SOME PAIRS OF POINTS WITH KNOWN DISSIMILARITYVALUETO LEARN ADISSIMILARITYMEASURE.THEN,WEUSETHEDISSIMILARITYMEASURETO GUIDE AN UNSUPERVISED FUZZY RELATIONAL CLUSTERING ALGORITHM. WE APPLY AND COMPARE TWO DIFFERENT METHODS FOR DISSIMILARITY EXTRACTION ON BOTH SYNTHETIC AND REAL DATA SETS. FURTHER, W E DISCUSS THE ADVANTAGES OF USING A NOVEL APPROACH, RECENTLY PROPOSED BY THE AUTHORS, TO RELATIONAL CLUSTERING THAT PARTITIONS THE DATA SET BASED ON THE PROXIMITY OF THE VECTORS CONTAINING THE DISSIMILARITY VALUES BETWEEN EACH PATTERN AND ALL THE OTHER PATTERNSIN THEDATASET. EXPERIMENTALRESULTSSHOW THAT,EVEN WITH ALOW PERCENTAGEOF KNOWN DISSIMILARITIES, THE COMBINATION LEARNING ALGORITHM/FUZZY RELATIONAL CLUSTERING ALGORITHM ALLOWSGENERATING TRUTHFULPARTITIONSOFTHEDATASETS.