Abstract: Handling complex data in uncertain environments has been a topic of extensive research interest. In order to solve the problem of dealing with multidimensional data under fuzzy linguistic environments and mining the rules contained between attributes described by linguistic values in different domains, an association rule extraction method based on a triadic fuzzy linguistic formal context was proposed. Firstly, a triadic fuzzy linguistic formal context was developed by combining a linguistic term set with triadic concept analysis theory. Subsequently, triadic fuzzy linguistic concepts were defined based on derivation operators, and an incremental construction method was applied to develop a knowledge discovery algorithm based on triadic fuzzy linguistic formal context, which acquired conceptual knowledge with semantic information under fuzzy triadic relations. Furthermore, the relationships between concepts were depicted using a triadic fuzzy linguistic diagram. Finally, an association rule extraction algorithm based on triadic fuzzy linguistic concepts was introduced to explore the correlations among attributes, resulting in semantic rules with conditional constraints. The experimental results show that the proposed method effectively handled multidimensional data under fuzzy linguistic environments, acquired conceptual knowledge with semantic information, and mined semantic rules with high credibility.

Key words: linguistic term set, triadic concept analysis, triadic fuzzy linguistic formal context, incremental construction, association rule

摘要： 在不确定性环境下，如何处理复杂数据一直受到广泛关注。对于模糊语言环境下的多维数据处理，不同领域中语言值描述的属性间蕴含的规则挖掘问题，提出一种基于三元模糊语言形式背景的关联规则提取方法。首先结合语言术语集与三元概念分析理论提出了三元模糊语言形式背景；随后基于诱导算子定义了三元模糊语言概念，并结合增量式构造思想给出了基于三元模糊语言形式背景的知识发现算法，获取了模糊三元关系下具有语义信息的概念知识，进而通过构建三元模糊语言图刻画出概念知识间的关系；最后，为了挖掘属性间的关联性，提出了基于三元模糊语言概念的关联规则提取算法，得到了具有条件约束的语义规则。通过对不同领域真实数据集进行实验分析，实验结果表明，所提方法可以有效处理模糊语言环境下的多维数据，获取到具有语义信息的概念知识，挖掘出具有高可信度的语义规则。

关键词: 语言术语集, 三元概念分析, 三元模糊语言形式背景, 增量式构造, 关联规则