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Comparison of Cluster Analysis Approaches for Binary Data

  • Giulia ContuEmail author
  • Luca Frigau
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Cluster methods allow to partition observations into homogeneous groups. Standard cluster analysis approaches consider the variables used to partition observations as continuous. In this work, we deal with the particular case all variables are binary. We focused on two specific methods that can handle binary data: the monothetic analysis and the model-based co-clustering. The aim is to compare the outputs performing these two methods on a common dataset, and figure out how they differ. The dataset on which the two methods are performed is a UNESCO dataset made up of 58 binary variables concerning the ability of UNESCO management to use Internet to promote world heritage sites.

Keywords

Cluster analysis Binary data Monothetic analysis cluster Model-based co-clustering UNESCO 

References

  1. 1.
    Bastida, U., Huan, T.C.: Performance evaluation of tourism websites’ information quality of four global destination brands: Beijing, Hong Kong, Shanghai, and Taipei. J. Business Res. 67(2), 167–170 (2014)CrossRefGoogle Scholar
  2. 2.
    Bhatia, P., Iovleff, S., Govaert, G.: blockcluster: an r package for model based co-clustering. J. Stat. Software 76(9), 1–24 (2017)CrossRefGoogle Scholar
  3. 3.
    Greenwood, M.C.: A comparison of plots for monothetic clustering, with applications to microbial communities and educational test development. Electron. J. Appl. Stat. Anal. 5(1), 1–14 (2012)MathSciNetGoogle Scholar
  4. 4.
    Ismail, N., Masron, T., Ahmad, A.: Cultural heritage tourism in malaysia: Issues and challenges. In: SHS Web of Conferences, vol. 12, p. 01059. EDP Sciences (2014)CrossRefGoogle Scholar
  5. 5.
    Kaufman, L., Rousseeuw, P.J.: Finding groups in data: an introduction to cluster analysis, vol. 344. Wiley, USA (2009)zbMATHGoogle Scholar
  6. 6.
    Law, R., Qi, S., Buhalis, D.: Progress in tourism management: a review of website evaluation in tourism research. Tourism management 31(3), 297–313 (2010)CrossRefGoogle Scholar
  7. 7.
    Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K.: cluster: Cluster analysis basics and extensions. R package version 1(4) (2016)Google Scholar
  8. 8.
    Patuelli, R., Mussoni, M., Candela, G.: The effects of world heritage sites on domestic tourism: a spatial interaction model for Italy. J. Geographical Syst. 15(3), 369–402 (2013)CrossRefGoogle Scholar
  9. 9.
    Richards, G.: Production and consumption of european cultural tourism. Ann. Tourism Res. 23(2), 261–283 (1996)CrossRefGoogle Scholar
  10. 10.
    World Heritage Committee: Operational guidelines for the implementation of the World heritage Convention. Unesco World Heritage Centre (2008)Google Scholar
  11. 11.
    Zhou, Q., DeSantis, R.: Usability issues in city tourism web site design: a content analysis. In: IPCC 2005. Proceedings. International Professional Communication Conference, 2005., 789–796. IEEE (2005)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Economics and BusinessUniversity of CagliariCagliariItaly

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