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Towards a Rough Classification of Business Travelers

  • Rob Law
  • Thomas Bauer
  • Karin Weber
  • Tony Tse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)

Abstract

The significant economic contributions of the fast growing tourism industry have drawn worldwide attention on understanding the behavioral and demographic patterns of visitors. This research makes an attempt to develop a rough sets based model that can capture the essential information from business travelers, a segment of the market that to date has been entirely overlooked by academic researchers in data mining. Utilizing the primary data collected from an Omnibus survey carried out in Hong Kong in late 2005, experimental findings showed that the induced decision rules could classify 82% of the cases in the testing set and 41% of the classified cases were correctly estimated. Most importantly, there was no statistically significant difference between the estimated values and actual values.

Keywords

Decision Attribute Information Table Tourism Management Business Traveler Rough Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Braun, B.M., Rungeling, B.: The relative economic impact of convention and tourist on a regional economy: a case study. International Journal Hospitality Management 11(1), 65–71 (1992)CrossRefGoogle Scholar
  2. 2.
    Grzymala-Busse, J.W., Goodwin, L.K., Zhang, X.: Increasing sensitivity of preterm birth by changing rule strengths. Pattern Recognition Letters 24, 903–910 (2003)CrossRefGoogle Scholar
  3. 3.
    Hong Kong Tourism Board: Statistics on Conventions & Exhibitions 2004 (2005a) (accessed February 3, 2006), available online at http://partnernet.hktourismboard.com/
  4. 4.
    Hong Kong Tourism Board: Visitor Profile Report 2004 (2005b) (accessed February 3, 2006), available online at http://partnernet.hktourismboard.com/
  5. 5.
    Hui, E.L.L., McKercher: Operational Issues in Marketing Research: An Example of the Omnibus Tourism Survey. Pacific Tourism Review 5(1/2), 5–13 (2001)Google Scholar
  6. 6.
    Katzberg, J., Ziarko, W.: Variable precision rough sets with asymmetric bounds. In: Ziarko, W. (ed.) Rough Sets, Fuzzy Sets and Knowledge Discovery (RSKD 1993), pp. 167–177. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  7. 7.
    Lawson, F.R.: Trends in business tourism management. Tourism Management 3(4), 298–302 (1982)CrossRefGoogle Scholar
  8. 8.
    Pawlak, Z.: Rough Set Elements. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, vol. 1, pp. 10–30. Physica-Verlag, Heidelberg, New York (1998)Google Scholar
  9. 9.
    Slowinski, R., Zopounidis, C.: Application of the Rough Set Approach to Evaluation of Bankruptcy Risk. Intelligent Systems in Accounting, Finance and Management 4, 27–41 (1995)CrossRefGoogle Scholar
  10. 10.
    Tanaka, H., Maeda, Y.: Reduction Methods for Medical Data. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, vol. 2, pp. 295–306. Physica-Verlag, Warsaw (1998)CrossRefGoogle Scholar
  11. 11.
    Ziarko, W.: Rough Sets. Journal of Computer and Systems Sciences 46, 39–59 (1993a)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Ziarko, W.: Variable Prevision Rough Set Model. Journal of Computer and Systems Sciences 46(1), 39–59 (1993b)CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Ziarko, W.: Rough Sets as a Methodology for Data Mining. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, vol. 1, pp. 554–576. Physica-Verlag, Heidelberg, New York (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rob Law
    • 1
  • Thomas Bauer
    • 1
  • Karin Weber
    • 1
  • Tony Tse
    • 1
  1. 1.School of Hotel & Tourism ManagementThe Hong Kong Polytechnic UniversityKowloonHong Kong

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