Title :
Fuzzy Clustering Level Analysis Using AIC Method for Large Size Samples
Author :
Kanagawa, Shuya ; Uesu, Hiroaki ; Shinkai, Kimiaki ; Tsuda, Ei ; Yamashita, Hajime
Author_Institution :
Musashi Inst. of Technol., Tokyo
Abstract :
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.
Keywords :
fuzzy set theory; information networks; maximum likelihood estimation; random number generation; Akaike information criterion; asymptotic normality; fuzzy clustering level analysis; large size samples; maximal likelihood estimator; pseudorandom numbers; Entropy; Fuzzy sets; Histograms; Information analysis; Maximum likelihood estimation; Probability density function; Random variables;
Conference_Titel :
Innovative Computing, Information and Control, 2007. ICICIC '07. Second International Conference on
Conference_Location :
Kumamoto
Print_ISBN :
0-7695-2882-1
DOI :
10.1109/ICICIC.2007.321
