Decision analysis of fuzzy partition tree applying AIC and fuzzy decision

Kimiaki Shinkai, Shuya Kanagawa, Takenobu Takizawa, Hajime Yamashita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We often use fuzzy graph to analyze inexact information such as sociogram structure ([1] and [2]). Concerning the hierarchical cluster analysis of a fuzzy graph ([3], [4] and [5] ), the number of clusters may have to be decided in the actual cluster analysis. In other word, we woud like to decide the optimal level with a partition tree. Concerning this problem, while AIC method in statistical analysis has been designed by us ([6] and [10]), we will now propose a fuzzy decision method which is based on the evaluation function paying attention to the size and number of clusters at each level.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings
Pages572-579
Number of pages8
EditionPART 3
DOIs
Publication statusPublished - 2008 Dec 24
Event12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008 - Zagreb, Croatia
Duration: 2008 Sep 32008 Sep 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume5179 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008
CountryCroatia
CityZagreb
Period08/9/308/9/5

Keywords

  • AIC (Akaike's information criterion)
  • Fuzzy decision
  • Fuzzy graph
  • Optimal level
  • Partition tree

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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