A Study on Time-of-Day Patterns for Internet User Using Recursive Partitioning Methods

  • Seong-Keon Lee
  • Seohoon Jin
  • Hyun-Cheol Kang
  • Sang-Tae Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


As of the remarkable increasing of internet users, there have been some demands of analyzing the users web accessing patterns. Internet related companies want to know the internet users web accessing patterns to promote their own products to the users. For analyzing customer’s time-of-day pattern for using internet as response vector that can be thought of as a discretized function, fitting ordinary decision trees may be unsuccessful because of their dimensionality. In this paper, we shall propose factor tree which would be more interpretable and competitive. Furthermore, using Korean internet company data, we will analyze time-of-day patterns for internet user.


Generalize Entropy Internet User Terminal Node Factor Tree Related Company 
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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  1. 1.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, CA (1984)Google Scholar
  2. 2.
    Hawkins, D.M., Kass, G.V.: Automatic Interaction Detection. In: Hawkins, D.H. (ed.) Topics in Applied Multivariate Analysis, Cambridge University Press, Cambridge (1982)Google Scholar
  3. 3.
    Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, California (1992)Google Scholar
  4. 4.
    Segal, M.R.: Tree-Structured Methods for Longitudinal Data. Journal of the American Statistical Association 87, 407–418 (1992)CrossRefGoogle Scholar
  5. 5.
    Yu, Y., Lambert, D.: Fitting Trees to Functional Data, With an Application to Time-of-Day Patterns. Journal of Computational and graphical Statistics 8, 749–762 (1999)CrossRefGoogle Scholar
  6. 6.
    Zhang, H.: Classification Trees for Multiple Binary Responses. Journal of the American Statistical Association 93, 180–193 (1998)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Seong-Keon Lee
    • 1
  • Seohoon Jin
    • 2
  • Hyun-Cheol Kang
    • 3
  • Sang-Tae Han
    • 3
  1. 1.Department of StatisticsSungshin Women’s UniversitySeoulKorea
  2. 2.Hyundai CapitalSeoulKorea
  3. 3.Department of Informational StatisticsHoseo University29-1, AsanKorea

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