A data reduction method in formal fuzzy contexts

As a basic operation in data mining and knowledge discovery, data reduction can reduce the volume of the data and simplify the representation of knowledge. In this paper we propose a method of attribute reduction in a formal fuzzy context based on the notion of one-sided fuzzy concept. According to the importance of attributes, we classify the attributes into three types: core attributes, relatively necessary attributes and unnecessary attributes, which are also referred to the attribute characteristics. We propose judgment theorems and the corresponding algorithms for computing the three types of attribute sets. Moreover, a straightforward attribute reduction method by virtue of attribute characteristics is formulated. We show that the computation of formal concepts on the reduced data set is made more efficient, and yet produces the same lattice structure and conceptual hierarchy as the ones derived from the original formal context.