Digestive casebase mining based on possibility theory and linear unidimensional scaling

Authors:
Anas Dahabiah;John Puentes;Basel Solaiman
Affiliations:
TELECOM Bretagne, Dértement Image et Traitement de l'Information, Brest, France and INSERM, Laboratoire de Traitement de l'Information M?cale, Brest, France;TELECOM Bretagne, Dértement Image et Traitement de l'Information, Brest, France and INSERM, Laboratoire de Traitement de l'Information M?cale, Brest, France;TELECOM Bretagne, Dértement Image et Traitement de l'Information, Brest, France and INSERM, Laboratoire de Traitement de l'Information M?cale, Brest, France
Venue:
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Year:
2009

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Abstract

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.