Incorporating both positive and negative association rules into the analysis of outbound tourism in Hong Kong

Li, Gang, Law, Rob, Rong, Jia and Vu, Huy Quan 2010, Incorporating both positive and negative association rules into the analysis of outbound tourism in Hong Kong, Journal of travel & tourism marketing, vol. 27, no. 8, pp. 812-828.

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Title Incorporating both positive and negative association rules into the analysis of outbound tourism in Hong Kong
Author(s) Li, Gang
Law, Rob
Rong, Jia
Vu, Huy Quan
Journal name Journal of travel & tourism marketing
Volume number 27
Issue number 8
Start page 812
End page 828
Total pages 17
Publisher Routledge
Place of publication Philadelphia, Pa.
Publication date 2010-12
ISSN 1054-8408
1540-7306
Keyword(s) contrast analysis
association rules
machine learning
data mining
Hong Kong
outbound tourism
Summary 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.
Language eng
Field of Research 150604 Tourism Marketing
Socio Economic Objective 900301 Economic Issues in Tourism
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2010
Copyright notice ©2010, Taylor & Francis Group, LLC
Persistent URL http://hdl.handle.net/10536/DRO/DU:30032950

Document type: Journal Article
Collection: School of Information Technology
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