Abstract
Despite numerous studies have applied various forecasting models to tourism demand analysis, data mining techniques have been largely overlooked by academic researchers in tourism forecasting prior to 1999. Based on our review of published articles in tourism journals that applied data mining techniques to tourism demand forecasting, we find that the application of data mining techniques are still at their infancy. This paper concludes with practical implications and future research areas.
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
Burger, C.J.S.C., Dohnal, M., Kathrada, M., Law, R.: A practitioners guide to a time-series methods for tourism demand forecasting – a case study of Durban, South Africa. Tourism Management 22, 403–409 (2001)
Chen, K.Y., Wang, C.H.: Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management 28, 215–226 (2007)
Chen, L.D.: A review of the Data Mining literature. In: Proceedings of the Hong Kong International Computer Conference 1997, pp. 23–31 (1997)
Cho, V.: A comparison of three different approaches to tourist arrival forecasting. Tourism Management 24, 323–330 (2003)
Decrop, A.: Tourists’ decision-making and behavior processes. In: Consumer Behavior in Travel and Tourism, Haworth Hospitality Press, New York (1999)
Delen, D., Sirakaya, E.: Determining the efficacy of Data-mining methods in predicting gaming ballot outcomes. Journal of Hospitality & Tourism Research 30(3), 313–332 (2006)
Frechtling, D.C.: Forecasting Tourism Demand: Methods and Strategies. Butterworth-Heinemann, Oxford (2001)
Galarza, M.G., Saura, I.G., Garcia, H.C.: Destination image: towards a conceptual framework. Annals of Tourism Research 29(1), 56–78 (2002)
Goh, C., Law, R.: Incorporating the rough sets theory into travel demand analysis. Tourism Management 24, 511–517 (2003)
Goh, C., Law, R., Mok, H.M.K.: Analyzing and forecasting tourism demand: A Rough Sets approach. Journal of Travel Research (forthcoming)
Heung, V.C.S., Qu, H., Chu, R.: The relationship between vacation factors and socio-demographic and characteristics: the case of Japanese leisure travelers. Tourism Management 22(3), 259–269 (2001)
Kon, S.C., Turner, L.W.: Neural network forecasting of tourism demand. Tourism Economics 11(3), 301–328 (2005)
Law, R., Au, N.: A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management 20, 89–97 (1999)
Law, R., Goh, C., Pine, R.: Modeling tourism demand: A decision rules based approach. Journal of Travel & Tourism Marketing 16(2/3), 61–69 (2004)
Law, R.: Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Management 21, 331–340 (2000)
Law, R.: Hospitality data mining myths. FIU Hospitality Review 16(1), 59–66 (1998)
Law, R.: The impact of the Asian Financial Crisis on Japanese demand for travel to Hong Kong: A study of various forecasting techniques. Journal of Travel & Tourism Marketing 10(2/3), 47–65 (2001)
McIntosh, R.W., Goeldner, C.R., Ritchie, J.R.B.: Tourism: Principles, Practices, Philosophies. John Wiley & Sons, New York (1995)
McKercher, B., Law, R., Lam, T.: Rating tourism and hospitality journals. Tourism Management 27(6), 1235–1252 (2006)
Pai, P.F., Hong, W.C.: An improved neural network model in forecasting arrivals. Annals of Tourism Research 32(4), 1138–1141 (2005)
Palmer, A., Montaño, J.J., Sesé, A.: Designing an artificial neural network for forecasting tourism time series. Tourism Management 27, 781–790 (2006)
Sami, A.: Obstacles and misunderstandings facing medical data mining. In: Li, X., Zaïane, O.R., Li, Z. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 856–863. Springer, Heidelberg (2006)
Um, S., Crompton, J.L.: Attitude determinants in tourism destination choice. Annals of Tourism Research 17(3), 432–448 (1990)
Uysal, M., El Roubi, S.E.: Artificial neural networks versus multiple regression in tourism demand analysis. Journal of Travel Research 38, 111–118 (1999)
Wang, C.H.: Predicting tourism demand using fuzzy time series and hybrid grey theory. Tourism Management 25, 367–374 (2004)
Witt, S.F., Witt, C.A.: Forecasting tourism demand: A review of empirical research. International Journal of Forecasting 11(3), 447–475 (1995)
Yu, G., Schwartz, Z.: Forecasting short time-series tourism demand with Artificial Intelligence models. Journal of Travel Research 45, 194–203 (2006)
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., Mok, H., Goh, C. (2007). Data Mining in Tourism Demand Analysis: A Retrospective Analysis. 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_47
Download citation
DOI: https://doi.org/10.1007/978-3-540-73871-8_47
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)