Clustering Algorithm Optimization Model for Essential Attribute Analysis of Tour Package Forms Inbound Tourism Market in Thailand
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Abstract
This research proposes the clustering algorithm optimization model for essential an attribute analysis of tour package forms for the market structure of inbound tourism in Thailand. The processes applied are the clustering algorithms with algorithm K-mean, algorithm hierarchical clustering, algorithm random clustering, and algorithm DBSCAN clustering. In addition, attribute analysis with evolutionary optimization algorithm and particle swarm optimization algorithm was performed. Tour packages of inbound tourism market of the official website of Tourism Authority of Thailand B.E. 2561 were gathered, encompassing 717 records on website, each web record contains 26 attributes. The results of clustering algorithm optimization process found that the Particle Swarm Optimization (PSO) algorithm performs the best and the best model is DBSCAN clustering of which 0.813 Davies-Bouldin index value decreases to 0.369, accounting for 54.612% increased efficiency value out of 10 clusters. Furthermore, an essential attribute analysis suggests five attributes in relation to tour package form adaptation, namely beach activities, sporting/golfing, Thai cooking activities, and other pursuits such as riding a long tail boat, taking a Tuk Tuk and getting engaged in a business activity. Research findings will help inbound tour operators develop tour packages in accordance with specific requirements of inbound tourism market.
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