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In the context of Web mining, clustering could be used to cluster similar click-streams to determine learning behaviours in the case of e-learning, or general site access behaviours in e-commerce. Most of the algorithms presented in the literature to deal with clustering Web sessions treat sessions as sets of visited pages within a time period and don't consider the sequence of the click-stream visitation. This has a significant consequence when comparing similarities between Web sessions. We propose in this paper a new algorithm based on sequence alignment to measure similarities between Web sessions where sessions are chronologically ordered sequences of page accesses.