Linguistic-valued layered concept lattice and its rule extraction

Formal concept analysis as an effective tool for data analysis and knowledge acquisition can be used to describe the potential relation between objects and attributes. In order to handle linguistic uncertainty information with comparability and incomparability, we propose a kind of linguistic-valued formal concept analysis approach based on lattice implication algebra. Firstly, by setting different linguistic-valued trust degrees, we put forward a linguistic-valued layered concept lattice for meeting the requirements of different experts at different levels. Secondly, the rule extraction algorithm of the linguistic-valued layered concept lattice with the trust degree is given to acquire non-redundant linguistic-valued rules with different trust degrees by using the linguistic-valued weakly consistent formal decision context. Then, aiming at the same premise or conclusion for the different rules, we adopt the deleting or uniting strategy to deal with the redundant rules. The updated and simplified rules can make the rule acquisition easier and the linguistic-valued decision rules extracted are more compact. Finally, the effectiveness and practicability of the proposed approach are illustrated by the comparison analysis.