Fuzzy clustering level analysis using AIC method for large size samples

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

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

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationSecond International Conference on Innovative Computing, Information and Control, ICICIC 2007
DOIs
Publication statusPublished - 2008
Event2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007 - Kumamoto
Duration: 2007 Sep 52007 Sep 7

Other

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

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ASJC Scopus subject areas

  • Computer Science(all)
  • Mechanical Engineering

Cite this

Kanagawa, S., Uesu, H., Shinkai, K., Tsuda, E., & Yamashita, H. (2008). Fuzzy clustering level analysis using AIC method for large size samples. In Second International Conference on Innovative Computing, Information and Control, ICICIC 2007 [4428036] https://doi.org/10.1109/ICICIC.2007.321