KNCM: Kernel Neutrosophic c-Means Clustering

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• 作者：Yaman Akbulut^a ; Abdulkadir Şengü ; r^a ; Yanhui Guo^b ; Kemal Polat^c ; ^{kemal_polat2003@yahoo.com}
• 关键词：Data clustering ; Fuzzy clustering ; Neutrosophic c-means ; Kernel function
• 刊名：Applied Soft Computing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：52
• 期：Complete
• 页码：714-724
• 全文大小：6283 K
• 卷排序：52

文摘

We proposed a new Kernel Neutrosophic c- Means (KNCM) algorithm for improving the NCM method on the nonlinearly separable datasets. In addition, new membership and prototype update equations were derived from minimization of the proposed cost function. The developed KNCM method was applied on variety of applications such as toy dataset clustering, real dataset clustering and noisy image segmentation. The obtained results were compared with the KFCM method. The obtained results showed that the proposed KNCM method yielded better results than KFCM.