Validation and interpretation of Web users' sessions clusters

Understanding users' navigation on the Web is important towards improving the quality of information and the speed of accessing large-scale Web data sources. Clustering of users' navigation into sessions has been proposed in order to identify patterns and similarities which are then managed in the context of Web users oriented applications (searching, e-commerce, etc.). This paper deals with the problem of assessing the quality of user session clusters in order to make inferences regarding the users' navigation behavior. A common model-based clustering algorithm is used to result in clusters of Web users' sessions. These clusters are validated by using a statistical test, which measures the distances of the clusters' distributions to infer their dissimilarity and distinguishing level. Furthermore, a visualization method is proposed in order to interpret the relation between clusters. Using real data sets, we illustrate how the proposed analysis can be applied in popular application scenarios to reveal valuable associations among Web users' navigation sessions.

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