Volume 33, Issue 2 (IJIEPR 2022)                   IJIEPR 2022, 33(2): 1-12 | Back to browse issues page

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zahiri S H, ghanbari N, Shahraki H. Clustering of Fuzzy Data Sets Based on Particle Swarm Optimization With Fuzzy Cluster Centers. IJIEPR 2022; 33 (2) :1-12
URL: http://ijiepr.iust.ac.ir/article-1-1326-en.html
Clustering of Fuzzy Data Sets Based on Particle Swarm Optimization With Fuzzy Cluster Centers
Seyed hamid Zahiri 1, Najme Ghanbari2 , Hadi Shahraki3
1- Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran , shzahiri@yahoo.com
2- Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
3- Department of Computer Engineering, Faculty of Industry and Mining, University of Sistan and Baluchestan, Khash, Iran
Abstract:   (2315 Views)
In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy numbers, a similarity criterion based on the intersection region of the fuzzy numbers is used.  The performance of the suggested clustering method has been experimented on both benchmark and artificial datasets. These datasets are used in the fuzzy form. The experiential results represent that the suggested clustering method with fuzzy cluster centers can cluster triangular fuzzy datasets like other standard uncertain data clustering methods. Experimental results demonstrate that, in almost all datasets, the proposed clustering method provides better results in accuracy when compared to Uncertain K-Means and Uncertain K-medoids algorithms.
Keywords: Clustering, Particle swarm clustering method, Uncertain data, Triangular fuzzy data, Fuzzy cluster centers, Similarity value.
Full-Text [PDF 1109 kb]   (984 Downloads)    
Type of Study: Research | Subject: Optimization Techniques
Received: 2021/09/14 | Accepted: 2022/02/26 | Published: 2022/06/30
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