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Analysis of Similarity Coefficients in Fuzzy Node Fuzzy Graph and Its Application

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4 Author(s)
Uesu, H. ; Waseda Univ., Tokyo, Japan ; Kanagawa, S. ; Shinkai, K. ; Nagashima, K.

Generally, we could efficiently analyze the inexact information and investigate the fuzzy relation by applying the fuzzy graph theory. We would extend the fuzzy graph theory, and propose a fuzzy node fuzzy graph. Since a fuzzy node fuzzy graph is complicated to analyze, we would transform it to a simple fuzzy graph by using T-norm family. In addition, to investigate the relations between nodes, we would define the fuzzy contingency table. In this paper, we would discuss about five subjects, (1) new T-norm "Uesu product", (2) fuzzy node fuzzy graph, (3) fuzzy contingency table, (4) entropy measures of fuzziness and (5) decision analysis of the optimal fuzzy graph GÉ0 in the fuzzy graph sequence {GÉ}. By using the fuzzy node fuzzy graph theory, the new T-norm and the fuzzy contingency table, we could clarify the relational structure of fuzzy information. According to the decision method in section 2, we could find the optimal fuzzy graph GÉ0 in the fuzzy graph sequence {GÉ}, and clarify the structural feature of the fuzzy node fuzzy graph. Moreover, we would illustrate its practical effectiveness with the case study concerning sociometry analysis.

Published in:

Innovations in Bio-Inspired Computing and Applications (IBICA), 2012 Third International Conference on

Date of Conference:

26-28 Sept. 2012