Sequential pattern mining of tourist from China

• Main Authors: Jian-Ming Lai, 賴建銘
• Other Authors: Dong-Her Shih
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/60413638273777691174

碩士 === 國立雲林科技大學 === 資訊管理系碩士班 === 101 === In recent years, Taiwan opens the door of tourism with China. In addition, China has become the largest inbound country for Taiwan when the backpacker of China is available. Tourism is an important platform which can offer two different cultures to exchange. Moreover, the opening tourism is also a great solution which can improve the negative impression and share the different social type between China and Taiwan. In addition, the growth for economic and opportunities of job is effectively when the opening tourism is widespread. There are many studies which are discussed for the tourism between China and Taiwan. These studies focus on the satisfaction of tourism and shopping, the motivation of travel, the revisiting willingness and explaining the questionnaire findings. However, the issue of tourism between China and Taiwan is seldom used by information technology. In this age of information explosion, how to extract the useful information is an important issue. Data mining is a popular technology which can find the useful information from large data. In this study, we aim to use the general data mining and sequential pattern mining to extract the pattern of the dataset about the tourism between China and Taiwan and understand the travel routes and scenic spots in Taiwan. The result shows that there is lots of changing about the cities, scenic spots and shopping places of Chinese tourists visit Taiwan. However, the matching result of sequence pattern shows that sequential patterns of cities, scenic spots and shopping places are significantly different especially in some specific months.