Relational structure analysis of fuzzy graph and its application: For analyzing fuzzy data of human relation

Hiroaki Uesu; Kenichi Nagashima; Hsunhsun Chung; Ei Tsuda

doi:10.1109/FUZZY.2011.6007573

Relational structure analysis of fuzzy graph and its application: For analyzing fuzzy data of human relation

Hiroaki Uesu^*, Kenichi Nagashima, Hsunhsun Chung, Ei Tsuda

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

Generally, we could efficiently analyze the inexact information and investigate the fuzzy relation by applying the fuzzy graph theory^[1]. 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) decision analysis of the optimal fuzzy graph G_λ0 in the fuzzy graph sequence {G_λ} and (5) its application to sociometry analysis. 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 G₀ in the fuzzy graph sequence {G _λ}, 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.

Original language	English
Title of host publication	IEEE International Conference on Fuzzy Systems
Pages	1593-1597
Number of pages	5
DOIs	https://doi.org/10.1109/FUZZY.2011.6007573
Publication status	Published - 2011
Externally published	Yes
Event	2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei Duration: 2011 Jun 27 → 2011 Jun 30

Other

Other	2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
City	Taipei
Period	11/6/27 → 11/6/30

Keywords

contingency table
fuzzy node fuzzy graph
optimal fuzzy graph
sociometry analysis
T-norm

ASJC Scopus subject areas

Software
Artificial Intelligence
Applied Mathematics
Theoretical Computer Science

Access to Document

10.1109/FUZZY.2011.6007573

Cite this

@inproceedings{7e1a6d72e60c4a2e9f606a8fd59d0b1c,

title = "Relational structure analysis of fuzzy graph and its application: For analyzing fuzzy data of human relation",

abstract = "Generally, we could efficiently analyze the inexact information and investigate the fuzzy relation by applying the fuzzy graph theory[1]. 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) decision analysis of the optimal fuzzy graph Gλ0 in the fuzzy graph sequence {Gλ} and (5) its application to sociometry analysis. 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 G0 in the fuzzy graph sequence {G λ}, 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.",

keywords = "contingency table, fuzzy node fuzzy graph, optimal fuzzy graph, sociometry analysis, T-norm",

author = "Hiroaki Uesu and Kenichi Nagashima and Hsunhsun Chung and Ei Tsuda",

year = "2011",

doi = "10.1109/FUZZY.2011.6007573",

language = "English",

isbn = "9781424473175",

pages = "1593--1597",

booktitle = "IEEE International Conference on Fuzzy Systems",

note = "2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 ; Conference date: 27-06-2011 Through 30-06-2011",

}

TY - GEN

T1 - Relational structure analysis of fuzzy graph and its application

T2 - 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011

AU - Uesu, Hiroaki

AU - Nagashima, Kenichi

AU - Chung, Hsunhsun

AU - Tsuda, Ei

PY - 2011

Y1 - 2011

N2 - Generally, we could efficiently analyze the inexact information and investigate the fuzzy relation by applying the fuzzy graph theory[1]. 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) decision analysis of the optimal fuzzy graph Gλ0 in the fuzzy graph sequence {Gλ} and (5) its application to sociometry analysis. 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 G0 in the fuzzy graph sequence {G λ}, 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.

AB - Generally, we could efficiently analyze the inexact information and investigate the fuzzy relation by applying the fuzzy graph theory[1]. 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) decision analysis of the optimal fuzzy graph Gλ0 in the fuzzy graph sequence {Gλ} and (5) its application to sociometry analysis. 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 G0 in the fuzzy graph sequence {G λ}, 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.

KW - contingency table

KW - fuzzy node fuzzy graph

KW - optimal fuzzy graph

KW - sociometry analysis

KW - T-norm

UR - http://www.scopus.com/inward/record.url?scp=80053056952&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053056952&partnerID=8YFLogxK

U2 - 10.1109/FUZZY.2011.6007573

DO - 10.1109/FUZZY.2011.6007573

M3 - Conference contribution

AN - SCOPUS:80053056952

SN - 9781424473175

SP - 1593

EP - 1597

BT - IEEE International Conference on Fuzzy Systems

Y2 - 27 June 2011 through 30 June 2011

ER -

Relational structure analysis of fuzzy graph and its application: For analyzing fuzzy data of human relation

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this