Fuzzy Clustering Level Analysis Using AIC Method for Large Size Samples
Shûya Kanagawa, Hiroaki Uesu, Kimiaki Shinkai, Ei Tsuda, Hajime Yamashita · 2007
This paper investigates the fuzzy clustering level analysis using AIC (Akaike's information criterion) method for small size samples. 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.