Abstract
This paper aims to analyze Couchsurfers on a hospitality tourism platform, the free homestay website Couchsurfing by utilizing fuzzy clustering. As a local host, the author has hosted 54 Couchsurfers in Kyoto, Japan, and collected a new dataset from each of them. The dataset includes 6 factors: Names, Accompanying people, Staying days, Hospitality degree, Cleanness degree, and Activeness degree of talking. Furthermore, in order to analyze different groups of Couchsurfers, a fuzzy clustering method based on fuzzy equivalence relation was used in this paper. In the clustering method, the dataset were standardized and built for fuzzy compatible matrix by max-min composition method first. Then, a fuzzy equivalence relation matrix was derived by square method transitive closure from the fuzzy compatible matrix. Based on the fuzzy equivalence relation matrix, 54 Couchsurfers who have stayed with the author were classified into 4 clusters: (1) Welcomed Couchsurfers; (2) Neutral Couchsurfers; (3) Not Welcomed Couchsurfers; (4) Speechless Couchsurfers. The 4 clusters represent different characteristics of different Couchsurfers and the author explained each cluster by his true experiences with them. Lastly, the author offers practical suggestions for future research.
Published Version
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