Conceptual Clustering Based on Linguistic-Valued Layered Concept Lattice

Formal concept analysis (FCA) is well suited to explain the meaning of clusters. However, most of the existing FCA-based concept clustering algorithms cannot handle fuzzy linguistic information. In this work, we propose a conceptual clustering based on linguistic-valued layered concept lattice is generated by the fuzzy linguistic coupling relationship between attributes. To obtain bi-clustering results for linguistic concepts, attribute-induced and object-induced matrices are first proposed. Second, the problem of overlapping clustering results is handled by calculating the stability of linguistic-valued layered concepts. In addition, the conceptual clustering results can be obtained at different granularities by varying the fuzzy linguistic-valued trust degrees. Finally, illustration example is used to demonstrate the effectiveness of our proposed method.