Summary: 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.
{© 2019 The Authors. International Transactions in Operational Research © 2019 International Federation of Operational Research Societies}