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

Fingerprint

Fuzzy clustering

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

Fuzzy clustering level analysis using AIC method for large size samples. / Kanagawa, Shuya; Uesu, Hiroaki; Shinkai, Kimiaki; Tsuda, Ei; Yamashita, Hajime.

Second International Conference on Innovative Computing, Information and Control, ICICIC 2007. 2008. 4428036.

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

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
Kanagawa S, Uesu H, Shinkai K, Tsuda E, Yamashita H. Fuzzy clustering level analysis using AIC method for large size samples. In Second International Conference on Innovative Computing, Information and Control, ICICIC 2007. 2008. 4428036 https://doi.org/10.1109/ICICIC.2007.321
Kanagawa, Shuya ; Uesu, Hiroaki ; Shinkai, Kimiaki ; Tsuda, Ei ; Yamashita, Hajime. / Fuzzy clustering level analysis using AIC method for large size samples. Second International Conference on Innovative Computing, Information and Control, ICICIC 2007. 2008.
@inproceedings{48545d9492c642e4b3ca64e757a2c8c3,
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",

}

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

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

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

ER -