Abstract
With the fast growing of the Internet and its Web users all over the world, how to manage and discover useful patterns from tremendous and evolving Web information sources become new challenges to our data engineering researchers. Also, there is a great demand on designing scalable and flexible data mining algorithms for various time-critical and data-intensive Web applications. In this paper, we purpose a new clustering model for generating and maintaining clusters efficiently which represent the changing Web user patterns in Websites. With effective pruning process, the clusters can be fast discovered and updated to reflect the current or changing user patterns to Website administrators. This model can also be employed in different Web applications such as personalization and recommendation systems.
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Wu, E.H., Ng, M.K., Yip, A.M., Chan, T.F. (2004). A Clustering Model for Mining Evolving Web User Patterns in Data Stream Environment. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_83
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DOI: https://doi.org/10.1007/978-3-540-28651-6_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
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