Abstract
Motivated by the demand for formalized representation of outcomes of data mining investigations and the successful results of using Formal Concept Analysis (FCA) and Ontology, this chapter addresses the task of constructing an ontology of data mining in order to support flexible query in large Database using FCA and Fuzzy Ontology. A new approach for automatic generation of Fuzzy Ontology of Data Mining (FODM), through the combination of conceptual clustering, fuzzy logic and FCA will be presented. Then, a new algorithm to support database flexible querying using the generated fuzzy ontology will be defined. The approach starts with the organization of the data in homogeneous clusters having common properties which allows to deduce the data's semantic. Then, it models these clusters by an extension of the FCA. This lattice will be used to build a core of ontology. This ontology will be represented, then, as a set of fuzzy rules as an efficient answers to flexible queries. We show that this approach is optimum because the evaluation of the query is not done on the set of starting data which is huge but rather by using the generated fuzzy ontology.