Fuzzy clustering level analysis using AIC method for large size samples

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

doi:10.1109/ICICIC.2007.321

Fuzzy clustering level analysis using AIC method for large size samples

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

^*Corresponding author for this work

Global Education Center

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Second International Conference on Innovative Computing, Information and Control, ICICIC 2007
DOIs	https://doi.org/10.1109/ICICIC.2007.321
Publication status	Published - 2008
Event	2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007 - Kumamoto Duration: 2007 Sept 5 → 2007 Sept 7

Other

Other	2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007
City	Kumamoto
Period	07/9/5 → 07/9/7

ASJC Scopus subject areas

Computer Science(all)
Mechanical Engineering

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10.1109/ICICIC.2007.321

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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, 2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007, Kumamoto, 07/9/5. https://doi.org/10.1109/ICICIC.2007.321

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title = "Fuzzy clustering level analysis using AIC method for large size samples",

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.",

author = "Shuya Kanagawa and Hiroaki Uesu and Kimiaki Shinkai and Ei Tsuda and Hajime Yamashita",

year = "2008",

doi = "10.1109/ICICIC.2007.321",

language = "English",

isbn = "0769528821",

booktitle = "Second International Conference on Innovative Computing, Information and Control, ICICIC 2007",

note = "2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007 ; Conference date: 05-09-2007 Through 07-09-2007",

}

TY - GEN

T1 - Fuzzy clustering level analysis using AIC method for large size samples

AU - Kanagawa, Shuya

AU - Uesu, Hiroaki

AU - Shinkai, Kimiaki

AU - Tsuda, Ei

AU - Yamashita, Hajime

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=39049165212&partnerID=8YFLogxK

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U2 - 10.1109/ICICIC.2007.321

DO - 10.1109/ICICIC.2007.321

M3 - Conference contribution

AN - SCOPUS:39049165212

SN - 0769528821

SN - 9780769528823

BT - Second International Conference on Innovative Computing, Information and Control, ICICIC 2007

T2 - 2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007

Y2 - 5 September 2007 through 7 September 2007

ER -

Fuzzy clustering level analysis using AIC method for large size samples

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