The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SYSTEMS
Transactions ID Number: 28-910
Full Name: Anas Dahabiah
Position: Ph.D. Candidate
Age: ON
Sex: Male
Address: Dahabiah, 227, Rue Jean JAURES, 29200, Brest, France
Country: FRANCE
Tel: 0033625563984
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E-mail address: dahabiah@hotmail.com
Other E-mails: anas.dahabiah@telecom-bretagne.eu
Title of the Paper: Possibilistic Pattern Recognition in a Digestive Database for Mining Imperfect Data
Authors as they appear in the Paper: Anas Dahabiah, John Puentes, Basel Solaiman
Email addresses of all the authors: anas.dahabiah@telecom-bretagne.eu, john.puentes@telecom-bretagne.eu, basel.solaiman@telecom-bretagne.eu
Number of paper pages: 12
Abstract: We propose in this paper a method based, on the one hand, on possibility theory to calculate the similarity among the objects of any casebase, taking into account the imperfection and the heterogeneity of data, and based, on the other hand, on the geometric models like the linear and the circular unidimensional scaling and on the graphic models like the ultrametric trees in order to represent and to visualize this similarity in such a way that we can explore and discover the potential structures and patterns that exist in the data. This approach will be applied to an endoscopic casebase in order to recognize the lesions and the pathologies of this base, and several concrete examples will be given along the paper in order to clarify the mathematical concepts of the method.
Keywords: Similarity, Possibility Theory, Linear and Circular Unidimensional Scaling, Ultrametric Trees Endoscopic Images, Data Mining.
EXTENSION of the file: .pdf
Special (Invited) Session: Digestive Casebase Mining Based on Possibility Theory and Linear Unidimensional Scaling
Organizer of the Session: 609416
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