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.

形式概念分析作为数据分析和知识获取的有效工具，可以用来描述对象和属性之间的潜在关系。为了处理具有可比性和不可比性的语言不确定性信息，我们提出了一种基于格蕴涵代数的语言价值形式概念分析方法。首先，通过设置不同的语言值信任度，提出了语言值分层概念格，以满足不同层次专家的需求。其次，给出了具有信任度的语言值分层概念格的规则提取算法，利用语言值弱一致的形式决策上下文获取具有不同信任度的非冗余语言值规则。然后，针对不同规则的相同前提或结论，我们采用删除或合并策略来处理冗余规则。更新和简化的规则可以使规则获取更容易，提取的语言值决策规则更加紧凑。最后，通过对比分析说明了所提方法的有效性和实用性。