Parallel FCM clustering algorithm of fuzzy number based on cut set

Article type: Research Article

Authors: Huang, Dakun | Wang, Jing | Wen, Zhicheng^*

Affiliations: Jiangxi University of Engineering, Xinyu, Jiangxi, China

Correspondence: [*] Corresponding author: Zhicheng Wen, Jiangxi University of Engineering, Xinyu, Jiangxi, China. Tel.: +86 15873372746; E-mail: [email protected].

Abstract: Aiming at the problem of complex method and low efficiency of fuzzy numbers in classification processing, a parallel Fuzzy CMeans (FCM) clustering method based on cut set is proposed. Firstly, according to the decomposition theorem, the fuzzy numbers are divided horizontally into the form of the union of interval numbers, and then the interval numbers are transformed into the determined “real” data, and the parallel FCM clustering algorithm is used to classify the fuzzy numbers. The theoretical analysis and application show that the method has good classification accuracy and efficiency for fuzzy data clustering.

Keywords: Fuzzy number, fuzzy C means clustering, interval number, influence factor

DOI: 10.3233/JCM-204838

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 21, no. 4, pp. 989-997, 2021