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The need for discovering knowledge from XML documents according to both structure and content features has become challenging , due to the increase in application contexts for which handling both structure and content information in XML data is essential. So, the challenge is to find an hierarchical structure which ensure a combination of data levels and(More)
Résumé. Quand il sera question de classifier des données massives, le temps de réponse, l’accès disque et la qualité des classes formées deviennent des enjeux majeurs pour les entreprises. C’est dans ce cadre que nous avons été amenés à définir un cadre de classification non supervisée des données hétérogènes à large échelle qui contribue à la résolution de(More)
In this study, we propose a new statical approach for high-dimensionality reduction of heterogenous data that limits the curse of dimensionality and deals with missing values. To handle these latter, we propose to use the Random Forest imputation’s method. The main purpose here is to extract useful information and so reducing the search space to facilitate(More)
The characteristics of XML (eXtensible Markup Language) documents have favored the need to develop specific and flexible querying systems while taking into account the coexistence of both structural and content information. The ultimate goal of these systems is to respond to different user expectations which tend to return appropriate answers to their(More)
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