Despite the numerous research endeavors aimed at examining tourists’destinations choice behavior by their motivations, it remains difficult for practitioners to utilize the results of association rule mining methods in tourism management. The purpose of this research were to explore and investigate tourist movement patterns of leisure, business and shopping travellers. This paper also aims to be a methodological contribution to the ?eld of spatial tourism behavior research by presenting an empirical study on the mining of association rules in tourist attraction visits. Using the dataset of 2013 international visitor survey collected by the Ministry of Culture, and Tourism in Korea, the data mining method was applied to over 10000 inbound tourists. We were able to identify interesting associations rules among three tourist groups and discover that three groups have a substantially different tendency of visiting destinations. First, according to the result of leisure travelers, who visited Korea for relaxation on their vacation, the N tower in Seoul particularly played a bridging role of other destinations in Seoul. Second, a geographical proximity between destinations was observed in business travelers. In addition, business travelers tended to visit destinations, where they could experience Korean traditional culture by visiting Insa-dong, and a Korean traditional village(Namsan Hanok Maeul) and also sought to visit a location such as Itaewon, where they could enjoy a multi-cultural atmosphere, that was not discovered in other groups. Thirdly, the result of shopping tourists revealed that they were likely to visit relatively few destinations compared to leisure and business travelers with a high record of their support ratio.
An extensive literature review and interpretation were followed by an exploratory analysis, analyzing the discovered associations rules each group and additionally visualizing them. This paper presents both academic and practical implications on dominantly different movement patterns of tourists based on their motivations. The result will help tourism private organizations develop better tour packages and more appropriate tourism products aligned to the characteristics of the tourists. The empirical results will be also useful in assisting tourism managers to make an appropriate decision and implement more efficient strategies.