A sample de-noising method for FCM clustering induced by Gauss kernel. (11th May 2021) - Wang, Yunxing; Li, Liyan; Wen, Zhicheng
Aiming at the problem of instability of clustering result because of the existence of noise samples in general FCM algorithm, an improved FCM algorithm induced by Gauss kernel induced is proposed. Firstly, the influence of samples distribution on clustering is analysed, and the Gauss kernel function is used to nonlinear map of the samples, then the improvement of the objective function of the FCM algorithm and clustering of samples are achieved, thus the purpose of suppressing noise is achieved. The experimental comparison shows that compared with other FCM algorithms, the proposed algorithm's successful classification rate is about 10% higher and the partition coefficient is about 10% lower than other FCM algorithms, indicating that the algorithm has higher clustering effectiveness.