Abstract
Determining the causal relation among attributes in a domain is a key task in the data mining and knowledge discovery. In this paper, we applied a causal discovery algorithm to the business traveler expenditure survey data [1]. A general class of causal models is adopted in this paper to discover the causal relationship among continuous and discrete variables. All those factors which have direct effect on the expense pattern of travelers could be detected. Our discovery results reinforced some conclusions of the rough set analysis and found some new conclusions which might significantly improve the understanding of expenditure behaviors of the business traveler.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Law, R., Bauer, T., Weber, K., Tse, T.: Towards a rough classification of business travelers. In: Li, X., Zaïane, O.R., Li, Z. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 135–142. Springer, Heidelberg (2006)
Cai, L.A., Hong, G.S., Morrison, A.: Household expenditure patterns for tourism products and services. Journal of Travel and Tourism Marketing 4, 15–40 (1995)
Fesenmaier, D.R., Jones, L., Um, S., Ozuna, T.: Assessing the economic impacts of outdoor recreation travel to the texas gulf coast. Journal of Travel Research 28, 18–23 (1989)
Pyo, S., Uysal, M., McLellan, R.: A linear expenditure model for tourism demand. Annals of Tourism Research 18 (1991)
Sheldon, P.: Forecasting tourism: Expenditure versus arrivals. Journal of Travel Research 32, 41–57 (1994)
Smeral, E.: Tourism demand, economic theory and econometrics: An integrated approach. Journal of Travel Research, 38–43 (1988)
Lawson, F.R.: Trends in business tourism management. Tourism Management 3, 298–302 (1982)
Braun, B., Rungeling, B.: The relative economic impact of convention and tourist on a regional economy: a case study. International Journal Hospitality Management 11, 65–71 (1992)
Li, G., Dai, H.: The discovery of generalized causal models with mixed variables using MML criterion. In: Proceedings of The Fourth SIAM International Conference on Data Mining, Lake Buena Vista, Florida, USA, SIAM (2004)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems, 2nd edn. Morgan Kauffmann Publishers, San Mateo, California (1988)
Wallace, C., Freeman, P.: Estimation and inference by compact coding. Journal of the Royal Statistical Society B, 49, 240–252 (1987)
Murphy, K.: The Bayes Net Toolbox for Matlab. Computer Science and Statistics 33, 331–351 (2001)
Law, R.: Hospitality data mining myths. FIU Hospitality Review 16, 59–66 (1998)
Athiyaman, A., Robertson, R.: Time series forecasting techniques: Short-term planning in tourism. International Journal of Contemporary Hospitality Management 4, 8–11 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Law, R., Li, G. (2007). A Causal Analysis for the Expenditure Data of Business Travelers. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_51
Download citation
DOI: https://doi.org/10.1007/978-3-540-73871-8_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73870-1
Online ISBN: 978-3-540-73871-8
eBook Packages: Computer ScienceComputer Science (R0)