Clustering tourist using DBSCAN algorithm

dc.contributor.authorFuangfar Pensiri
dc.contributor.authorPorawat Visutsak
dc.contributor.authorOrawan Chaowalit
dc.contributor.correspondenceO. Chaowalit; King Mongkut's University of Technology North Bangkok University, Bangkok, 1518 Pracharat 1 Road Wongsawang Bangsue, 10800, Thailand; email: orawan@su.ac.th
dc.date.accessioned2025-03-10T07:35:06Z
dc.date.available2025-03-10T07:35:06Z
dc.date.issued2022
dc.description.abstractThe tourist clustering refer the aggregating of prospective tourist into different groups with common observance by using statistical data analysis technique. In this paper, we apply the Density-based spatial clustering of applications with noise (DBSCAN) to find the factors that can segment the tourist associated with using digital technology equipment as a tourism facility based on the data of tourist behaviour and activity. We describe the methodology, firstly analyse the algorithm. Secondly, compare execution of the different parameter values (Eps): the maximum radius of the neighbourhood from core point and the minimum number of points required to form a dense region (MinPts). Finally, examine the outcome of the application, Tourist's career and tourism style are two factors from eleven factors can cluster the tourists into eight groups with Eps and MinPts parameters 0.5 and 10 respectively. © 2022 Author(s).
dc.identifier.citationAIP Conference Proceedings
dc.identifier.doi10.1063/5.0082995
dc.identifier.isbn978-073544339-6
dc.identifier.issn0094243X
dc.identifier.scopus2-s2.0-85132847987
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4627
dc.languageEnglish
dc.publisherAmerican Institute of Physics Inc.
dc.rights.holderScopus
dc.titleClustering tourist using DBSCAN algorithm
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85132847987&doi=10.1063%2f5.0082995&partnerID=40&md5=1e3fe976422b36ba8645ac27f4a5f33f
oaire.citation.volume2471
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