Research Reports

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Author: search.filters.author.Kouici, Salima

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    A Clustering Application for the Web Usage Mining
    (CERIST, 2012-12) Kouici, Salima
    The Web Usage Mining constitutes a new branch of the Web Mining. It allows the study of the behavior of both users and potential customers via their site navigation. The mainly used source for the Web Usage Mining is the servers Log Files. A Log File contains an important mass of data, including user’s information (username, used software, etc.) and all the queries he has made on the website (requested files, the number of bytes transferred, time spent on each page, the page of entry to the site .... etc.). In this work we shall outline an application, made on this type of data, which is based on a clustering method, namely KMEANS. This application allows the definition of homogeneous groups constituting users profiles so that to anticipate the needs and with a view of communication adapted to each segment of users. In this application we have recorded some technical problems. These problems concerns the data cleaning (removing queries of images and multimedia files associated with web pages, removing queries from search bots... etc.) and the setting up of visitor sessions, knowing that a session is a sequence of pages viewed by the same user.
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    Citation structural modeling and some bibliometric indicators
    (CERIST, 2008-07) Harik, Hakim; Dahmane, Madjid; Kouici, Salima
    The evaluation of the scientific production constitutes an important step for the decision-makers as far as the conception and the implementation of the policies relating to science and technology. This contribution, being a part in the bibliometry field, calls for some structural elements of the graph theory in order to achieve the development of bibliometric indicators on the citation basis.
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    The choice of similarity measure for Agglomerative Hierarchical Clustering
    (CERIST, 2011-06) Kouici, Salima
    This paper studies the generalization property of inter-classes similarity measures. After, three equivalence classes of inter-elements similarity measures are defined and some consequences of the measures choice on Agglomerative Hierarchical Clustering results areproved.
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    Sur les mesures de similarité usuelles des données binaires
    (CERIST, 2010-05) Kouici, Salima
    L'étude de quelques propriétés des mesures de similarité usuelles des données binaires est proposée. Cette étude peut aider dans le choix d'une mesure dans le cadre d'une méthode de classification. Par la suite, les mesures usuelles sont comparées avec une mesure structurelle, notée S.
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    Distance entre classes d'éléments : cas des données binaires
    (CERIST, 2008) Kouici, Salima; Khelladi, Abdelkader
    Une nouvelle mesure de resemblance entre deux classes (parties) d'éléments d'un ensemble N est proposée. Cette mesure permet de considérer tous les éléments contenus dans les deux classes en question pour son calcul. Elle concerne les données décrites par un ensemble de caractéristiques présentes/absentes (binaires). Elle vérifie les propriétés d'une distante sur l'ensemble P(N), l'ensemble des parties de $N$. Elle est utilisée pour la résolution d'un problème de classification des données binaires.