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Communication Dans Un Congrès Année : 2009

Digestive casebase mining based on possibility theory and linear unidimensional scaling

Résumé

In this paper, we present a very general and powerful approach that enables us to mine easily depending on the concept of similarity any casebase consisting of a large number of objects (cases) containing heterogeneous, imperfect and missing data by organizing and gathering these objects into meaningful groups in such a way that efficient analysis and retrieval of information could be easily achieved. Our method is based essentially on possibility theory and on the linear unidimensional scaling representation and is applied on a real digestive database.
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hal-02118529 , version 1 (03-05-2019)

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  • HAL Id : hal-02118529 , version 1

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Anas Dahabiah, John Puentes, Basel Solaiman. Digestive casebase mining based on possibility theory and linear unidimensional scaling. Recent Advances in Artificial Intelligence, Knowledge Engineering and Data Base, 21-23 Februrary, Cambridge, United Kingdom, May 2009, Cambridge, United Kingdom. pp.218 - 223. ⟨hal-02118529⟩
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