Fuzzy Information Analysis Applying AIC Method

Shuya Kanagawa; Hiroaki Uesu; Kimiaki Shinkai; Ei Tsuda

doi:10.14864/softscis.2006.0.1818.0

SCIS & ISIS 2006

Session ID : SA-C3-2

DOI https://doi.org/10.14864/softscis.2006.0.1818.0

Conference information

Host: Japan SOciety for Fuzzy Theory and intelligent informatics

Co-host: The Korea Fuzzy Logic and Intelligent Systems Society, IEEE Computational Intelligence Society, The International Fuzzy Systems Association, 21th Century COE Program "Creation of Agent-Based Social Systems Sciences"

SA-C3 Soft Science

Fuzzy Information Analysis Applying AIC Method

*Shuya Kanagawa, Hiroaki Uesu, Kimiaki Shinkai, Ei Tsuda

Author information

Keywords: Fuzzy clustering, Fuzzy graph, AIC

CONFERENCE PROCEEDINGS FREE ACCESS

Details

Abstract

As for analysis of inexact information such as human behavior, mental process, social structure and so on, we consider fuzzy graph of some relation in these problems. Fuzzy graph is constructed with clusters of some levels. Furthermore, recently Uesu and Shinkai et. al [6] - [8] proposed a rational method to decide the optimal level of fuzzy clustering with partition tree. In this paper we improve their methods to use AIC (Akaike's information criterion) which is a likelihood estimator via Kulback-Leibler information number.

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