, Almerys est un acteur majeur dans le domaine du traitement industriel des données numériques et est un opérateur de tiers-payant santé

, Le travail de [2] permet de faire de l'identification sur Internet en se basant sur des données de navigation web multi-siteségalementsiteségalement. Elle utilise commé eléments caractéristiques du comportement de l'internaute : le nom de domaine visité, le nombre de pages vues, l'heure de démarrage d'une session et sa durée. Le jeu de données, collecté par un fournisseur de services, est constitué des 300premì eres connections de 2798 utilisateurs sur une période de 12 mois. Les profils utilisateurs sont composés d'itemsets fréquents de taille 1, extraits avec l'algorithme Apriori (cf, pas suffisantes pour authentifier ou distinguer les utilisateurs. La taille de la base de données est aussi un paramètre déterminant qui pourrait expliquer pourquoi leur modèle n'est pas capable de fournir de bons résultats

, En effet, un temps de pause de 25, 5 minutes permet de définir une session. A partir d'un ensemble de sessions d'URL, les auteurs en déduisent le nombre de fois qu'une page a ´ eté demandée, le nombre de fois qu'une page a ´ eté lapremì ere page d'une session et enfin le nombre de fois qu'une page a ´ eté ladernì ere page d'une session. Les auteurs proposent une modélisation des parcours sur un site sous forme de grammaire probabiliste hypertextuelle ou en anglais Hypertext Probabilistic Grammar (HPG) qui permet de fouiller des bases de parcours et d'identifier des séquences 9 récurrentes suivies par les internautes. Les auteurs utilisent un modèlè a base de N-grammes (cf, L'auteur compare deux approches de sélection d'itemsets fréquents en particulier : le support et le lift. L'´ etude montre que pour des comportements anonymes de petite taille (jusqu'` a une vingtaine de sites visités) les modèles les plus efficaces restent des modèles de classification traditionnels comme les arbres de décision, vol.135

, Une séquence utilise le principe de précédence c'est-` a-dire chaqué elément de la liste est précédé desélémentsdeséléments qui l

, 2 Motivations : limites des approches existantes, p.95

. .. Classification-na¨?vena¨?ve-bayésienne, 99 3.3.1 Adaptation simple de la classification na¨?vena¨?ve bayésienne . . 99 3.3.2 Classification na¨?vena¨?ve bayésiennè a base d'itemsets fermés fréquents discriminants

, ClassificationàClassificationà base d'itemsets fermés fréquents discriminants

. .. Mesures-de-distance,

.. .. Résumé,

, Ladeuxì eme boucle indique le nombre d'informations porté par chaque flux de test (une seule session, ou bien une agrégation de plusieurs sessions). Enfin, latroisì eme boucle permet de lisser les résultats en créant plusieurs jeux d'exécution (cf. random subsampling). Dans la table 5.3, nous fixons X ` a 100. La boucle ligne 10 calcule les itemsets fermés fréquents spécifiquesspécifiques`spécifiquesà chaque utilisateur etétablitetétablit les vecteurs de profil. La boucle ligne 13 calcule la valeur de chaque vecteur de profil utilisateur

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