Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30629
Title: Incorporating both positive and negative association rules into the analysis of outbound tourism in Hong Kong
Authors: Li, G
Law, R 
Rong, J
Vu, HQ
Keywords: Contrast analysis
Association rules
Machine learning
Data mining
Hong Kong
Outbound tourism
Issue Date: 2010
Publisher: Routledge, Taylor & Francis Group
Source: Journal of travel & tourism marketing, 2010, v. 27, no. 8, p. 812-828 How to cite?
Journal: Journal of travel & tourism marketing 
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.
URI: http://hdl.handle.net/10397/30629
ISSN: 1054-8408
EISSN: 1540-7306
DOI: 10.1080/10548408.2010.527248
Appears in Collections:Journal/Magazine Article

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