| Summary: | 碩士 === 國立澎湖科技大學 === 觀光休閒事業管理研究所 === 103 === Abstract
The objectives of this research are (1) to investigate the characteristics and behaviors of tourists who visited Phuket of Thailand and (2) to suggest the efficient approach of analyzing business data that is different in both characteristics and behaviors. In this study, two different clustering methods are selected. This study compares the performances of two stage clustering methods including SOM followed by K-Means algorithm and Hierarchical clustering followed by K-Means algorithm. There are ten factors used in clustering including zone, country, travel, province, type of accommodation, number of night, gender, age, propose of travel, career, annual income, and cost of travel and fee. By using S.E.Mean and root mean square standard deviation (SMSSTD) of each clusters as criteria in selection the numbers of cluster for segmentation.
Results show that the appropriate number of clusters in segmentation is ten by using SOM and K-Means, while the number is six by using the second method. Clustering from both methods show that the majority of tourists are from Europe. The other categories reveals the information, such as travel by BTS, MRT or taxi and travel by domestic airliner. Most of the tourists choose to stay at hotel in a long time. Money they earn an average annual are moderate. But they have expenses are quite high in each day.Their purposes of visiting are for vacation during the holidays. and most of the tourists are professional. Based on the analysis, it can be concluded that the second approach has higher performance than the first one since it requires less execution time in clustering and provides more homogeneity among data within each cluster
Keywords: Clustering, Data Mining, Classification, Tourism
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