A Fast Algorithm for Updating Negative Concept Lattices with Increasing the Granularity Sizes of Attributes

Abstract

In this paper, firstly, we studied the relationship between negative concept lattices with increasing the granularity sizes of the attributes. Aiming to do this, negative concepts and covering relations were both classified into three types, and the sufficient and necessary conditions of distinguishing these kinds of negative concepts and covering relations are given, respectively. Further, based on the above analysis, an algorithm for updating negative concept lattices after the increase is proposed. Finally, the experimental results demonstrated that our algorithm performed significantly better than the direct construction algorithm.