Incorporating Both Positive and Negative Association Rules into the Analysis of Outbound Tourism in Hong Kong

ABSTRACT

This article presents a novel approach to data mining that incorporates both positive and negative association rules into the analysis of outbound travelers. Using datasets collected from three large-scale domestic tourism surveys on Hong Kong residents' outbound pleasure travel, different sets of targeted rules were generated to provide promising information that will allow practitioners and policy makers to better understand the important relationship between condition attributes and target attributes. This article will be of interest to readers who want to understand methods for integrating the latest data mining techniques into tourism research. It will also be of use to marketing managers in destinations to better formulate strategies for receiving outbound travelers from Hong Kong, and possibly elsewhere.

Keywords:

• Contrast analysis
• association rules
• machine learning
• data mining
• Hong Kong
• outbound tourism

The authors would like to thank the constructive comments offered by the three anonymous reviewers on improving an earlier version of this article. The research described in this article was partly supported by a grant funded by The Hong Kong Polytechnic University.