Abstract
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.
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Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, CA (1984)
Hawkins, D.M., Kass, G.V.: Automatic Interaction Detection. In: Hawkins, D.H. (ed.) Topics in Applied Multivariate Analysis, Cambridge University Press, Cambridge (1982)
Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, California (1992)
Segal, M.R.: Tree-Structured Methods for Longitudinal Data. Journal of the American Statistical Association 87, 407–418 (1992)
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)
Zhang, H.: Classification Trees for Multiple Binary Responses. Journal of the American Statistical Association 93, 180–193 (1998)
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© 2006 Springer-Verlag Berlin Heidelberg
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Lee, SK., Jin, S., Kang, HC., Han, ST. (2006). A Study on Time-of-Day Patterns for Internet User Using Recursive Partitioning Methods. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_107
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DOI: https://doi.org/10.1007/11941439_107
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
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