Rule Reductions of Decision Formal Context Based on Mixed Information

Three-way concept lattice extracts knowledge from positive and negative information separately. However, some applications require to mix positive and negative information for management and representation explicitly. In this paper, rule acquisition of FCA is extended based on mixed information. Firstly, two types of mixed concept lattices are studied. Then the relationships between mixed concept and classical concept, three-way concept are explored in depth. Secondly, mixed decision rules are investigated thoroughly. And the weak-basis is put forward to approximate to the basis of mixed decision rules. Finally, the comparison of mixed decision rules and three-way decision rules is perfectly discussed.

The Method of Data Analysis in Intuitionistic Fuzzy Generalized Consistent Decision Formal Context

Concept lattice has been successfully applied to various fields as an effective tool for data analysis and knowledge discovery, with attribute reduction being the key problem. This paper combines the intuitionistic fuzzy theory with the concept lattice theory and proposes one kind of concept lattice in intuitionistic fuzzy generalized consistent decision formal context. Furthermore, an approach to attribute a reduction in the discernibility matrix is proposed and investigated, making the discovery of implicit knowledge easier and the representation simpler in the data system and perfecting the theory of concept lattice. Moreover, this paper studies, in detail, the algorithms and case study of data analysis in the intuitionistic fuzzy generalized consistent decision formal context. The potential value of the method to deal with information discussed in this paper, especially the value of forecasting and decision-making, is expected in future.