Attribute Exploration with Fuzzy Attributes and Background Knowledge

Attribute exploration is a formal concept analytical tool for knowledge discovery by interactive determination of the implications holding between a given set of attributes. The corresponding algorithm queries the user in an ecient way about the implications between the attributes. The result of the exploration process is a representative set of examples for the entire theory and a set of implications from which all implications that hold between the considered attributes can be deduced. The method was successfully applied in dierent real-life applications for discrete data. In many instances, the user may know some implications before the exploration starts. These are considered as background knowl- edge and their usage shortens the exploration process. In this paper we show that the handling of background information can be generalised to the fuzzy setting.