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Fuzzy clustering based on linguistic information: a case study on clustering destinations with tourists’ perceptions
Author(s) -
Han ZhiQiu,
Yang WuE,
Wang YingMing,
Ma ChaoQun
Publication year - 2020
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/itor.12721
Subject(s) - cluster analysis , fuzzy clustering , computer science , semantics (computer science) , sample (material) , fuzzy set , perception , artificial intelligence , data mining , fuzzy logic , psychology , chemistry , chromatography , neuroscience , programming language
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.