Abstract
One of the main issues in Web usage mining is the discovery of patterns in the navigational behavior of Web users. Standard approaches, such as clustering of users’ sessions and discovering association rules or frequent navigational paths, do not generally allow to characterize or quantify the unobservable factors that lead to common navigational patterns. Therefore, it is necessary to develop techniques that can discover hidden and useful relationships among users as well as between users and Web objects. Correspondence Analysis (CO-AN) is particularly useful in this context, since it can uncover meaningful associations among users and pages. We present a model-based cluster analysis for Web users sessions including a novel visualization and interpretation approach which is based on CO-AN.
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Pallis, G., Angelis, L., Vakali, A. (2005). Model-Based Cluster Analysis for Web Users Sessions. In: Hacid, MS., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds) Foundations of Intelligent Systems. ISMIS 2005. Lecture Notes in Computer Science(), vol 3488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11425274_23
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DOI: https://doi.org/10.1007/11425274_23
Publisher Name: Springer, Berlin, Heidelberg
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