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題名 | Comparison of Two-Stage Clustering Methods: SOM and K-Means Algorithm and Hierarchical Clustering and K-Means Algorithm in Tourist Information Management in Phuket=以兩階段集群分析方法之比較:以泰國普吉島遊客資訊管理為例 |
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作者 | Worawut, Dabpimsri; Wirot, Yotsawat; Worawut, Dabpimsri; Wirot, Yotsawat; | 書刊名 | 島嶼觀光研究 |
卷期 | 8:4 2015.12[民104.12] |
頁次 | 頁32-48 |
分類號 | 992.014 |
關鍵詞 | 群集分析; 資料探勘; 分類; 觀光; Clustering; Data mining; Classification; Tourism; |
語文 | 英文(English) |
中文摘要 | 本研究目的為調查到泰國普吉島旅遊遊客的屬性,了解其特質及行為上的差異,提供資訊分析作商業經營上之所需。本研究採用SOM and K-Means演算法及Hierarchical Clustering and K-Means演算法分析,並比較之研究結果列舉12項集群因素,包含區域、國別、旅遊、省別、住宿型態、住宿天數、性別、年齡、旅遊目的、職業、年所得與開銷。以三種不同形態到普吉島的觀光客作為分析依據,以標準差作為各集群分析數量的標準。研究結果顯示,以SOM and K-Means演算法分析,最適宜的集群組數為十組;Hierarchical Clustering and K-Means演算法的組數為六組。兩種方法皆顯示遊客最大來源為歐洲,其他的發現包含遊客旅遊方式多採空中捷運方式、捷運或計程車及國內航線。多數遊客選擇入住旅館,中高收入、入住中高價位旅館並且會待在旅館一段時間。旅遊目的為利用假期進行度假旅遊。整體而論,第二種分析方法進行群集時間較短且有群集內的資料有較好的均質性,故較第一種方法為佳。 |
英文摘要 | The objectives of this research are to investigate the characteristics and behaviors of tourists who visited Phuket of Thailand and 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 (RMSSTD) 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. Both methods show that the majority of tourists are from Europe. The other categories reveal the information, such as travel by BTS, MRT or taxi and travel by domestic airliner. The majority 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 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. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。