To improve the neutrosophic C-means clustering performance on noise image segmentation of non-convex irregular data

neutrosophic C-means clustering in kernel space is presented. Fuzzy C-means clustering algorithm is widely used in image segmentation because of its simplicity. However

the fuzzy C-means clustering algorithm directly clusters pixel value of different location

which leads to huge sample number. Fuzzy C-means clustering algorithm does not consider spatial neighborhood information of pixels

and directly uses gray value of sample to make clustering result in poor anti-noise performance. In this study

to overcome the limitations of fuzzy C-means clustering algorithm

neutrosophic C-means set is introduced into traditional fuzzy C-mean clustering algorithm. The neutrosophic C-means clustering algorithm is advantageous to effective segmentation of noisy or singular data

and its result is good for boundary segments of the sample. The clustering algorithm of neutrosophic fuzzy C-means using Euclidean distance is not suitable for clustering of complex structured data. Thus

use of nonlinear function data samples are mapped to high-dimensional feature space and wisdom of fuzzy C-means clustering algorithm. Kernel function concept is introduced into the neutrosophic C-means clustering algorithm. By using nonlinear problem that satisfies Mercer condition

nonlinear transformation is used to map non-separable

linear input mode space of low-dimensional space to a separable

high-dimension

linear feature space. ntroduced the concept of kernel function.By using the nonlinear problem that satisifies the Mercer condition

The nonlinear transformation is used to map the liner non separable input pattern space of the low dimensional space of the low dimensional space to a first separable high dimension feature space. Kernel Hilbert space theory is a nonlinear function of samples that are mapped to high-dimensional feature space

changing data distribution characteristics and into its neutrosophic C-means clustering algorithm. This theory is proposed in the clustering algorithm of kernel space neutrosophic C-means. Numerous image segmentation experiments to compare the results found in kernel space

which is obtained by neutrosophic fuzzy clustering algorithm

improve the existing algorithm for clustering with noise robustness and classification performance. Salt-and-pepper

Gaussian

mixed

and multiplicative noise tests are conducted on four kinds of C-means clustering algorithms

namely

fuzzy

kernel fuzzy

neutrosophic

and kernel neutrosophic. Then

peak signal sizes to noise ratio of the four kinds of image segmentation algorithms are compared. With different additives and multiplicative noises added to a large amount of image segmentation test

a new algorithm division has been proven to have a significant effect and good robustness

and its validity is verified.