Robustness of classical fuzzy C-means (FCM)

B.I. Nasution; R. Kurniawan

doi:10.1109/ICOIACT.2018.8350729

Robustness of classical fuzzy C-means (FCM)

B.I. Nasution, R. Kurniawan

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Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

Classical Fuzzy C-Means (FCM) was believed as a robust clustering method when it is optimized and modified. But, at this time many researchers stated that classical FCM is less robust. So this study aims to investigate and prove the robustness of FCM by conducting studies into several data sets and optimization methods and modifications. The results show that FCM is a robust-proven method when viewed from the value of the objective function, the number of iterations, and the time being completed

Original language	Undefined
Title of host publication	2018 International Conference on Information and Communications Technology, ICOIACT 2018
Pages	321-325
Number of pages	5
DOIs	https://doi.org/10.1109/ICOIACT.2018.8350729
Publication status	Published - 2018

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10.1109/ICOIACT.2018.8350729

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title = "Robustness of classical fuzzy C-means (FCM)",

abstract = "Classical Fuzzy C-Means (FCM) was believed as a robust clustering method when it is optimized and modified. But, at this time many researchers stated that classical FCM is less robust. So this study aims to investigate and prove the robustness of FCM by conducting studies into several data sets and optimization methods and modifications. The results show that FCM is a robust-proven method when viewed from the value of the objective function, the number of iterations, and the time being completed",

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N2 - Classical Fuzzy C-Means (FCM) was believed as a robust clustering method when it is optimized and modified. But, at this time many researchers stated that classical FCM is less robust. So this study aims to investigate and prove the robustness of FCM by conducting studies into several data sets and optimization methods and modifications. The results show that FCM is a robust-proven method when viewed from the value of the objective function, the number of iterations, and the time being completed

AB - Classical Fuzzy C-Means (FCM) was believed as a robust clustering method when it is optimized and modified. But, at this time many researchers stated that classical FCM is less robust. So this study aims to investigate and prove the robustness of FCM by conducting studies into several data sets and optimization methods and modifications. The results show that FCM is a robust-proven method when viewed from the value of the objective function, the number of iterations, and the time being completed

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M3 - Conference contribution

SP - 321

EP - 325

BT - 2018 International Conference on Information and Communications Technology, ICOIACT 2018

ER -

Robustness of classical fuzzy C-means (FCM)

Abstract

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