经典模糊c均值(FCM)的鲁棒性

2018 International Conference on Information and Communications Technology (ICOIACT) Pub Date : 2018-03-06 DOI:10.1109/ICOIACT.2018.8350729

B. I. Nasution, R. Kurniawan

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引用次数: 5

摘要

对经典模糊c均值(FCM)方法进行优化和改进，证明其具有较好的鲁棒性。但是，此时许多研究人员认为经典FCM的鲁棒性较差。因此，本研究旨在通过对多个数据集以及优化方法和修改的研究来调查和证明FCM的鲁棒性。结果表明，从目标函数的取值、迭代次数和完成时间来看，FCM是一种鲁棒性被证明的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Robustness of classical fuzzy C-means (FCM)

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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2018 International Conference on Information and Communications Technology (ICOIACT)

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