Data mining and fuzzy modeling

Fuzzy models are constructs relying heavily on a qualitative domain knowledge and diverse optimization techniques. What makes them different from other models is their inherent embedding in the context of nonnumeric set or fuzzy set-oriented information. One can also look at the development of the fuzzy models from the perspective of data mining-a prudent and user-oriented sifting of data, qualitative observations and calibration of commonsense rules in an attempt to establish meaningful and useful relationships between system's variables. Having accepted this point of view, we analyze various methods of fuzzy clustering and make them uniform enough so that they can constitute a viable design platform. Several fuzzy clustering methods (especially Fuzzy C-Means) have been already exploited in the context of fuzzy modelling. Our claim is that these methods need some conceptual shift that makes them possible to cope with a notion of "directionality" of any model, namely its ability to determine the values of the output variable(s) given the actual values of the inputs and state variables. This aspect of directionality along with the assumed specificity of modelling, is addressed in depth and leads to a series of detailed algorithms.