The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 89-467
Full Name: Anas Dahabiah
Position: Researcher
Age: ON
Sex: Male
Address: Dahabiah, 227 Rue Jean JAURES, 29200, Brest, France
Country: FRANCE
Tel: 0033625563984
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Fax:
E-mail address: anas.dahabiah@telecom-bretagne.eu
Other E-mails: dahabiah@hotmail.com
Title of the Paper: Gastroenterology Dataset Clustering Using Possibilistic Kohonen Maps
Authors as they appear in the Paper: Anas Dahabiah, John Puentes, and Basel Solaiman
Email addresses of all the authors: anas.dahabiah@telecom-bretagne.eu, dahabiah@hotmail.com
Number of paper pages: 14
Abstract: Kohonen maps are an efficient mechanism in signal processing and data mining applications. However, all the existing versions and approaches of this special type of neural networks are still incapable to efficiently handle within a simple, fast, and unified framework, the imperfection of the patterns' information elements on the one hand like the uncertainty, the missing data, etc., and the heterogeneity of their measuring scale (qualitative, quantitative, ordinal, etc.) on the other hand. Therefore, we propose in this paper a possibilistic Kohonen network essentially based on two fuzzy measures: the possibility and the necessity degrees, to deal with all these aspects together in a robust way. Concrete examples and medical applications will also be given to clarify and to easily explain the proposed algorithm.
Keywords: Self-Organizing Maps (SOMs), Fuzzy Logic (Possibility Theory), Imperfect information, Gastroenterology Dataset, Similarity
EXTENSION of the file: .pdf
Special (Invited) Session: Possibilistic Kohonen Maps
Organizer of the Session: 640-554
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