A sample de-noising method for FCM clustering induced by Gauss kernel
by Yunxing Wang; Liyan Li; Zhicheng Wen
International Journal of Information and Communication Technology (IJICT), Vol. 18, No. 4, 2021

Abstract: 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.

Online publication date: Fri, 11-Jun-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information and Communication Technology (IJICT):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com