Rule acquisition of three-way semi-concept lattices in formal decision context

Three-way concept analysis is an important tool for information processing,and rule acquisition is one of the research hotspots of three-way concept analysis.However,compared with three-way concept lattices,three-way semi-concept lattices have three-way operators with weaker constraints,which can generate more concepts.In this article,the problem of rule acquisition for three-way semi-concept lattices is discussed in general.The authors construct the finer relation of three-way semi-concept lattices,and propose a method of rule acquisition for three-way semi-concept lattices.The authors also discuss the set of decision rules and the relationships of decision rules among object-induced three-way semi-concept lattices,object-induced three-way concept lattices,classical concept lattices and semi-concept lattices.Finally,examples are provided to illustrate the validity of our conclusions.

Influence of Attribute Granulation on Three-Way Concept Lattices

In formal concept analysis based applications, controlling the structure of concept lattice is of vital importance, especially for big data, and is achieved via clarifying the granularity of attributes. Existing approaches for solving this issue are within the framework of classical formal concept analysis, which focuses on positive attributes. However, experiments have demonstrated that both positive and negative attributes exert comparable influence on knowledge discovery. Thus, it is essential to explore the granularity of attributes in positive and negative perspectives altogether. As a solution, we investigate this problem within the framework of three-way concept analysis. Specifically, we present zoom-in and zoom-out algorithms to obtain more particular and abstract three-way concepts, separately. Furthermore, we provide illustrative examples to show the practical significance of this study.