Fuzzy clustering based on linguistic information: a case study on clustering destinations with tourists’ perceptions

A fuzzy clustering method with linguistic information is introduced. It uses a minimizing cross‐entropy model to avoid setting the clustering threshold artificially. During the clustering, the semantics of the linguistic information is conservatively represented by solving a programming. It maximizes the potential differences between the objects to be clustered, and further helps an analyst to reach a semantics‐robust clustering result. A case study on clustering a sample destination set, which includes 13 Asia Pacific regions, based on a group of tourists’ perceptions is also proposed.