Alzennyr Da Silva
ABSTRACT
The growing number of traces left behind user transactions on the Internet (e.g. customer purchases, user navigations, etc.) has increased the importance of Web usage data analysis. A notable challenge of this analysis is the fact that the way in which a website is visited can evolve over time. As a result, the usage models must be continuously updated in order to reflect the current behaviour of the visitors. In this article, we introduce CAMEUD, a clustering approach to mine and detect changes in evolving usage data. The proposed approach is totally independent from the clustering algorithm applied in the classification problem and is able to detect and determine the nature of changes undergone by the usage groups (appearance, disappearance, fusion and split) at subsequent time intervals. Experiments on synthetic and real usage data sets evaluate the efficiency of CAMEUD.
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Index Terms
- CAMEUD: clustering approach for mining evolving usage data
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