Reliability-based of robust fuzzy flustering

• Published in: Kongzhi lilun yu yingyong Vol. 38; no. 4; p. 516
• Main Authors: Pan, Jin-yan, Gao, Peng, Gao, Yun-long, Xie, You-wei, Xiong, Yu-hui
• Format: Journal Article
• Language: Chinese
• Published: Guangzhou South China University of Technology 01.04.2021
• Subjects: Algorithms; Clustering; Datasets; Image segmentation; Noise sensitivity; Outliers (statistics); Reliability analysis; Robustness (mathematics)
• ISSN: 1000-8152
• DOI: 10.7641/CTA.2020.00480

Compared with the k-means algorithm, fuzzy C-means (FCM) considers the interaction between different data clusters by introducing fuzzy membership degree, thus avoiding the clustering center overlapping problem. However, fuzzy membership degree has the structural characteristics of trailing and warp-tail, which makes FCM algorithm very sensitive to noise points and outliers. In addition, the FCM algorithm tends to classify the data cluster with average size, so it is sensitive to data cluster size also, which makes the algorithm not good for clustering imbalanced data clusters. To solve these problems, a reliability–based of robust fuzzy clustering algorithm (RRFCM) is proposed in this paper. The algorithm is based on the current clustering results, the reliability analysis was carried out on the sample points, using the reliability of the sample points and local neighbor information, highlight the separability between different data clusters, so as to improve the robustness of the algorithm for noises, and r