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              <p>Tous les domaines de la science et de la technologie produisent gros volumes de données hétérogènes. L'exploration de tels volumes de données reste toujours un défi. Peu de travaux ciblent l'exploration et l'analyse de données séquentielles multidimensionnelles et hétérogènes. Dans ce travail, nous présentons une nouvelle approche appelée MMISP pour extraire des motifs séquentiels à partir de données séquentielles multidimensionnelles et hétérogènes, selon différents niveaux de granularité dépendant de connaissances extérnes. L'approache MMISP a été appliquée à l'analyser de trajectoires de soins de santé de patients en oncologie. Les données proviennent d'une base de données médico-administrative incluant toutes les informations sur les hospitalisations des patients dans la région Lorraine (France).</p>
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