Decision analysis of fuzzy partition tree applying AIC and fuzzy decision

被引：0

作者：

Shinkai, Kimiaki ^{[1
]}

Kanagawa, Shuya ^{[2
]}

Takizawa, Takenobu ^{[1
]}

Yamashita, Hajime ^{[1
]}

机构：

[1] Waseda Univ, Grad Sch Educ, Dept Math, Tokyo, Japan

[2] Musashi Inst Technol, Fac Engn, Tokyo, Japan

来源：

KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 3, PROCEEDINGS | 2008年 / 5179卷

关键词：

fuzzy graph; partition tree; ATC (Akaike's information criterion); fuzzy decision; optimal level;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：572 / +

页数：2

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