Structure analysis of fuzzy node fuzzy graph and its application to sociometry analysis

Hiroaki Uesu

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

We could generally analyze the inexact information efficiently 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. And we transform it to a fuzzy graph by using T-norm family. In this paper, we would discuss about four subjects, (1) fuzzy node fuzzy graph, (2) new T-norm "quasi logical product", (3) decision analysis of the optimal fuzzy graph in the fuzzy graph sequence {G λ }. By using the fuzzy node fuzzy graph theory and this new T-norm, we could clarify the relational structure of fuzzy information, and by using the decision of an optimal level on a partition tree, we could analyze the clustering relation among nodes. Moreover, we would illustrate its practical effectiveness with the case study concerning sociometry analysis.

本文言語English
ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ページ84-91
ページ数8
5179 LNAI
PART 3
DOI
出版ステータスPublished - 2008
イベント12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008 - Zagreb
継続期間: 2008 9 32008 9 5

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 3
5179 LNAI
ISSN(印刷版)03029743
ISSN(電子版)16113349

Other

Other12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008
CityZagreb
Period08/9/308/9/5

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 理論的コンピュータサイエンス

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