Fuzzy clustering level analysis using AIC method for large size samples

Shuya Kanagawa, Hiroaki Uesu, Kimiaki Shinkai, Ei Tsuda, Hajime Yamashita

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

In [3] we investigated fuzzy clustering level analysis using AIC (Akaike's information criterion) method for small size samples in Fig.I. Since AIC is obtained by the asymptotic normality for the maximal likelihood estimator, it is difficult to apply it to small size samples. Therefore, in the paper, we would show that the AIC method can be applied to large size samples which are constructed by a simulation with pseudo random numbers obeying several distributions.

本文言語English
ホスト出版物のタイトルSecond International Conference on Innovative Computing, Information and Control, ICICIC 2007
DOI
出版ステータスPublished - 2008
イベント2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007 - Kumamoto
継続期間: 2007 9 52007 9 7

Other

Other2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007
CityKumamoto
Period07/9/507/9/7

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 機械工学

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