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JACIII Vol.24 No.6 pp. 738-749
doi: 10.20965/jaciii.2020.p0738
(2020)

Paper:

Noise Rejection Approaches for Various Rough Set-Based C-Means Clustering

Seiki Ubukata, Sho Sekiya, Akira Notsu, and Katsuhiro Honda

Osaka Prefecture University
1-1 Gakuen-cho, Nakaku, Sakai, Osaka 599-8531, Japan

Received:
March 20, 2020
Accepted:
August 21, 2020
Published:
November 20, 2020
Keywords:
rough set theory, noise clustering, rough C-means, rough set C-means, rough membership C-means
Abstract
Noise Rejection Approaches for Various Rough Set-Based <i>C</i>-Means Clustering

Cluster boundaries by noise RMCM

In the field of cluster analysis, rough set-based extensions of hard C-means (HCM; k-means) including rough C-means (RCM), rough set C-means (RSCM), and rough membership C-means (RMCM) are promising approaches for dealing with the certainty, possibility, uncertainty of belonging of object to clusters. Since C-means-type methods are strongly affected by noise, noise clustering approaches have been proposed. In noise clustering approaches, noise objects, which are far from any cluster center, are rejected for robust estimation. In this paper, we introduce noise rejection approaches for rough set-based C-means based on probabilistic memberships and propose noise RCM with membership normalization (NRCM-MN), noise RSCM with membership normalization (NRSCM-MN), and noise RMCM (NRMCM). In addition, visualization demonstration of the cluster boundaries on the two-dimensional plane of the proposed methods is carried out to confirm the characteristics of each method. Furthermore, the clustering performance is verified by numerical experiments using real-world datasets.

Cite this article as:
Seiki Ubukata, Sho Sekiya, Akira Notsu, and Katsuhiro Honda, “Noise Rejection Approaches for Various Rough Set-Based C-Means Clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.6, pp. 738-749, 2020.
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