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<dc:title xml:lang="fr">Extraction d'informations synthétiques à partir de données séquentielles : application à l'évaluation de la qualité des rivières</dc:title>
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<dc:subject xml:lang="fr">Fouille de données séquentielles</dc:subject>
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<dc:subject xml:lang="fr">Hydrobiologie</dc:subject>
<dc:subject xml:lang="en">Sequential data mining</dc:subject>
<dc:subject xml:lang="en">Sequential patterns</dc:subject>
<dc:subject xml:lang="en">Partially ordered patterns</dc:subject>
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<dcterms:abstract xml:lang="fr">L'exploration des bases de données temporelles à l'aide de méthodes de fouille de données adaptées a fait l'objet de nombreux travaux de recherche. Cependant le volume d'informations extraites est souvent important et la tâche d'analyse reste alors difficile. Dans cette thèse, nous présentons des méthodes pour synthétiser et filtrer l'information extraite. L'objectif est de restituer des résultats qui soient interprétables. Pour cela, nous avons exploité la notion de séquence partiellement ordonnée et nous proposons (1) un algorithme qui extrait l'ensemble des motifs partiellement ordonnés clos; (2) un post-traitement pour filtrer un ensemble de motifs d'intérêt et(3) une approche qui extrait un consensus comme alternative à l'extraction de motifs. Les méthodes proposées ont été testées sur des données hydrobiologiques issues du projet ANR Fresqueau et elles ont été implantées dans un logiciel de visualisation destiné aux hydrobiologistes pour l'analyse de la qualité des cours d'eau.</dcterms:abstract>
<dcterms:abstract xml:lang="en">Exploring temporal databases with suitable data mining methods have been the subject of several studies. However, it often leads to an excessive volume of extracted information and the analysis is difficult for the user. We addressed this issue and we specically focused on methods that synthesize and filter extracted information. The objective is to provide interpretable results for humans. Thus, we relied on the notion of partially ordered sequence and we proposed (1) an algorithm that extracts the set of closed partially ordered patterns ; (2) a post-processing to filter some interesting patterns for the user and (3) an approach that extracts a partially ordered consensus as an alternative to pattern extraction. The proposed methods were applied for validation on hydrobiological data from the Fresqueau ANR project. In addition, they have been implemented in a visualization tool designed for hydrobiologists for water course quality analysis.</dcterms:abstract>
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