Mining Tourist Motive for Marketing Development via Twice-Learning

REFERENCES

• Burger, C. J. S. C., M. Dohnal, M. Kathrada, and R. Law. 2001. A practitioners guide to time-series methods for tourism demand forecasting–A case study of Durban, South Africa. Tourism Management 22(4):403–409.
   Web of Science ®Google Scholar
• Celotto, E., A. Ellero, P. Ferretti. 2012. Short-medium term tourist services demand forecasting with rough set theory. Procedia Economics and Finance 3:62–67.
   Google Scholar
• Chen, C.-F., M.-C. Lai, and C.-C. Yeh. 2012. Forecasting tourism demand based on empirical mode decomposition and neural network. Knowledge-Based Systems 26:281–287.
   Web of Science ®Google Scholar
• Chen, K.-T., and C.-H. Wang. 2007. Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management 28(1):215–226.
   Web of Science ®Google Scholar
• Chen, K.-Y. 2011. Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Applications 38(8):10368–10376.
   Web of Science ®Google Scholar
• Chi, C. G.-Q., and H. Qu. 2008. Examining the structural relationships of destination image, tourist satisfaction and destination loyalty: An integrated approach. Tourism Management 29(4):624–636.
   Web of Science ®Google Scholar
• Choi, T.-M., S.-C. Liu, K.-M. Pang, and P.-S. Chow. 2008. Shopping behaviors of individual tourists from the Chinese mainland to Hong Kong. Tourism Management 29(4):811–820.
   Web of Science ®Google Scholar
• Emel, G. G., C. Taskin, and Ö Akat. 2007. Profiling a domestic tourism market by means of association rule mining. Anatolia 18(2):334–342.
   Google Scholar
• Goh, C., and R. Law. 2003. Incorporating the rough sets theory into travel demand analysis. Tourism Management 24(5):511–517.
   Web of Science ®Google Scholar
• Gross, M. J., and G. Brown. 2008. An empirical structural model of tourists and places: Progressing involvement and place attachment into tourism. Tourism Management 29(6):1141–1151.
   Web of Science ®Google Scholar
• Hansen, L. K., and P. Salamon. 1990. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(10):993–1001.
   Web of Science ®Google Scholar
• Hong, W.-C., Y. Dong, L.-Y. Chen, and S.-Y. Wei, 2011. SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Applied Soft Computing 11(2):1881–1890.
   Web of Science ®Google Scholar
• Hsu, C. H. C., L. A. Cai, and M. Li. 2010. Expectation, motivation, and attitude: A tourist behavioral model. Journal of Travel Research 49(3):282–296.
   Web of Science ®Google Scholar
• Hsu, T. K., Y.-F. Tsai, and H.-H. Wu. 2009. The preference analysis for tourist choice of destination: A case study of Taiwan. Tourism Management 30(2):288–297.
   Web of Science ®Google Scholar
• Jalilvand, M. R., N. Samiei, B. Dini, and P. Y. Manzari. 2012. Examining the structural relationships of electronic word of mouth, destination image, tourist attitude toward destination and travel intention: An integrated approach. Journal of Destination Marketing and Management 1(1–2):134–143.
   Google Scholar
• Jiang, Y., M. Li, and Z.-H. Zhou. 2006. Generation of comprehensible hypotheses from gene expression data. In Proceedings of the International Workshop on Data Mining for Biomedical Application, Singapore, 116–123.
   Google Scholar
• Kim, S. S., D. J. Timothy, and J. Hwang. 2011. Erratum to: Understanding Japanese tourists’ shopping preferences using the decision tree analysis method. Tourism Management 32(3):544–554.
   Web of Science ®Google Scholar
• Kon, S. C., and W. L. Turner. 2005. Neural network forecasting of tourism demand. Tourism Economics 11(3):301–328.
   Google Scholar
• Law, R. 2000. Back–propagation learning in improving the accuracy of neural network–based tourism demand predicting. Tourism Management 21(4):331–340.
   Web of Science ®Google Scholar
• Law, R., and N. Au. 1999. A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management 20(1):89–97.
   Web of Science ®Google Scholar
• Lee, T. H. 2009. A structural model to examine how destination image, attitude, and motivation affect the future behavior of tourists. Leisure Science 31(3):215–236.
   Web of Science ®Google Scholar
• Li, G., R. Law, J. Rong, and Q. Huy Vu. 2010. Incorporating both positive and negative association rules into the analysis of outbound tourism in Hong Kong. Journal of Travel & Tourism Marketing 27(8):812–828.
   Web of Science ®Google Scholar
• Li, M., H. Li, and Z.-H. Zhou. 2009. Semi-supervised document retrieval. Information Processing & Management 45(3):341–355.
   Google Scholar
• Li, M., and Z.-H. Zhou. 2007. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Transactions on Systems, Man and Cybernetics – Part A: Systems and Humans 37(6):1088–1098.
   Google Scholar
• Melián-González, A., S. Moreno-Gil, and J. E. Araña. 2011. Gay tourism in a sun and beach destination. Tourism Management 32(5):1027–1037.
   Web of Science ®Google Scholar
• Palmer, A., J. J. Montaño, and A. Sesé. 2006. Designing an artificial neural network for forecasting tourism time series. Tourism Management 27(5):781–790.
   Web of Science ®Google Scholar
• Pattie, D. C., and J. Snyder. 1996. Using a neural network to forecast visitor behavior. Annals of Tourism Research 23(1):151–164.
   Web of Science ®Google Scholar
• Quinlan, J. R. 1993. C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
   Google Scholar
• Rong, J., H. Q. Vu, R. Law, and G. Li. 2011. A behavioral analysis of web sharers and browsers in Hong Kong using targeted association rule mining. Tourism Management 33(4):731–740.
   Web of Science ®Google Scholar
• Song, H., and G. Li. 2008. Tourism demand modeling and forecasting–A review of recent research. Tourism Management 29(2):203–220.
   Web of Science ®Google Scholar
• Valle, P. O., P. Pintassilgo, A. Matias, and F. André. 2012. Tourist attitudes towards an accommodation tax earmarked for environmental protection: A survey in the Algarve. Tourism Management 33(6):1408–1416.
   Web of Science ®Google Scholar
• Witten, I. H., and E. Frank. 2000. Data mining: Practical machine learning tools and techniques with Java implementations. San Francisco, CA: Morgan Kaufmann.
   Google Scholar
• Zhang, C., and J. Zhang. 2012. Mining tourist preferences with twice-learning. Lecture Notes on Artificial Intelligence 7104:483–493.
   Google Scholar
• Zhang, C., and J. Zhang. 2014. Analyzing Chinese citizens’ intentions of outbound travel: A machine learning approach. Current Issues in Tourism 17(7):592–609.
   Web of Science ®Google Scholar
• Zhou, Z.-H., K. J. Chen, and H. B. Dai. 2006. Enhancing relevance feedback in image retrieval using unlabeled data. ACM Transactions on Information Systems 24(2):219–244.
   Web of Science ®Google Scholar
• Zhou, Z.-H., and Y. Jiang. 2003. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine 7(1):37–42.
   PubMed Web of Science ®Google Scholar
• Zhou, Z.-H., and Y. Jiang. 2004. NeC4.5: Neural ensemble based C4.5. IEEE Transactions on Knowledge and Data Engineering 16(6):770–773.
   Web of Science ®Google Scholar