Clustering tourist using DBSCAN algorithm
Date
2022
ISBN
978-073544339-6
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Resource Type
Conference paper
Publisher
American Institute of Physics Inc.
Journal Title
Clustering tourist using DBSCAN algorithm
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Abstract
The 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).
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AIP Conference Proceedings