Abstract
The web contains rich and dynamic collections of hyperlink information, web page access, and usage information providing rich sources for data mining. From this, we need a system to recommend a visitor good information. This recommendation system can be constructed by web usage mining process. The web usage mining mines web log records to discover user access patterns of web pages. Also it is the application of data mining techniques to large web log data in order to extract usage patterns from user’s click streams. In general, the size of web log records is so large that we have difficulty to analyze web log data. To make matter worse, the web log records are very sparse. So it is very hard to estimate the dependency between the web pages. In this paper, we solved this difficulty of web usage mining using support vector machine. In the experiments, we verified our proposed method by given data from UCI machine learning repository and KDD cup 2000.
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References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceeding of the ACM SIGMOD International Conference on Management of Data (1993)
Casella, G., Berger, R.L.: Statistical Inference. Duxbury Press, MA (1990)
Conversano, C., Cappelli, C.: Missing data incremental imputation through tree based methods. In: 14th Conference on Computational Statistics, pp. 24–28 (2002)
Cooley, R., Mobasher, B., Srivastava, J.: Web Mining: Information and Pattern Discovery on the World Wide Web. In: Proceeding of the 9th IEEE International Conference on Tools with Artificial Intelligence (1997)
Fletcher, R.: Practical Methods of Optimization. John Wiley & Sons, Chichester (1989)
Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Haykin, S.: Neural Networks. Prentice-Hall, Englewood Cliffs (1999)
Hoaglin, D.C., Mosteller, F., Tukey, J.W.: Understanding robust and exploratory data analysis. John Wiley & Sons, Chichester (2000)
Lavori, R.J.A., Dawson, R., Shera, D.: A Multiple Imputation Strategy for Clinical Trials with Truncation of Patent Data. Statistics in Medicine 14, 1913–1925 (1995)
Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data. Wiley Inter-Science, Chichester (2002)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Resnick, P., Lacovou, N., Suchak, M., Berfstrom, P., Riedl, J.: GroupLens: An Open Architecture for collaborative filtering of Netnews. In: Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work (1994)
Rubin, D.B.: Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons, Chichester (1987)
Schafer, J.L.: Analysis of Incomplete Multivariate Data. Chapman and Hall, Boca Raton (1997)
Smola, A.J.: Regression estimation with support vector learning machines, Master’s thesis, Technische University (1996)
Vapnik, V.Z.: Statistical Learning Theory. John Wiley & Sons, Inc, Chichester (1998)
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Jun, SH. (2005). Web Usage Mining Using Support Vector Machine. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_43
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DOI: https://doi.org/10.1007/11494669_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
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