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. Avec-ce-chapitre, Dans cette partie nous avons présenté les solutions apportées concernant deux grands sujets : l'extraction de motifs séquentiels approximatifs dans les ux de données et la détection d'anomalies dans les ux de données, La partie suivante présente les conclusions, une exploitation dans le monde réel et les nombreuses perspectives ouvertes par ce travail

C. Séquence, PAGEACCUEIL -internet -chat Séquence 3 = PAGEACCUEIL -CARREFOURINFO -sport Séquence 4 = internet actu -chat Séquence 5 = PAGEACCUEIL -CARREFOURINFO -CARREFOURINFO Ces séquences, selon le degré de similitude choisi, devraient être regroupées en deux clusters : Cluster 1 : Séquence 1 = PAGEACCUEIL -CARREFOURINFO -sport Séquence

P. Séquence-5 and =. Cluster, Séquence 2 = PAGEACCUEIL -internet -chat Séquence 4 = internet actu -chat Pour chaque cluster, l'application fournira un représentant, permettant i) de le résumer et ii) de le comparer à la prochaine séquence à classer. Ce représentant sera le résultat d'une technique d'alignement appliquée au contenu de chaque cluster (i.e. aux séquences de navigation de ce cluster). Ainsi

. Exemple, Considérons le cluster suivant

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