Abstract
Fuzzy models are constructs relying heavily both 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. The role of fuzzy sets in knowledge discovery has not been visible even though they are inherently inclined towards coping with linguistic domain knowledge. The paper re-examines the key issues of knowledge discovery by putting them in the context of the technology of fuzzy sets. Subsequently, we reveal several interesting links between fuzzy data mining and fuzzy sets. The study is also geared toward a knowledge-oriented and context-based modification of well known fuzzy clustering.