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Communication Dans Un Congrès Année : 2009

Possibilistic Evidential Clustering

Résumé

An approach for clustering objects containing imperfect and heterogeneously-assigned data is proposed. This approach depends mainly on possibility theory to estimate the similarity between objects, and on belief theory and multidimensional scaling methods to assign relevant classes to them. This unsupervised clustering method has been applied to a medical database and robust results have been obtained with the absence of any a priori medical knowledge, and without knowing the key attributes of the concerned pathologies.
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Dates et versions

hal-02117914 , version 1 (02-05-2019)

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  • HAL Id : hal-02117914 , version 1

Citer

Anas Dahabiah, John Puentes, Basel Solaiman. Possibilistic Evidential Clustering. Recent Advances in Artificial Intelligence, Knowledge Engineering and Data Base, Februrary 21-23, Cambridge, UK, Feb 2009, Cambridge, United Kingdom. pp.212 - 217. ⟨hal-02117914⟩
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