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Ghanbari, N., Zahiri, S. H., Shahraki, H.. (1402). Clustering of Triangular Fuzzy Data based on Heuristic Methods. فناوری آموزش, (), 1-14. doi: 10.22061/jecei.2023.9641.645
N. Ghanbari; S. H. Zahiri; H. Shahraki. "Clustering of Triangular Fuzzy Data based on Heuristic Methods". فناوری آموزش, , , 1402, 1-14. doi: 10.22061/jecei.2023.9641.645
Ghanbari, N., Zahiri, S. H., Shahraki, H.. (1402). 'Clustering of Triangular Fuzzy Data based on Heuristic Methods', فناوری آموزش, (), pp. 1-14. doi: 10.22061/jecei.2023.9641.645
Ghanbari, N., Zahiri, S. H., Shahraki, H.. Clustering of Triangular Fuzzy Data based on Heuristic Methods. فناوری آموزش, 1402; (): 1-14. doi: 10.22061/jecei.2023.9641.645

Clustering of Triangular Fuzzy Data based on Heuristic Methods

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 24 خرداد 1402    اصل مقاله (1.87 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2023.9641.645
نویسندگان
N. Ghanbari1؛ S. H. Zahiri* 2؛ H. Shahraki3
1Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
2Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
3Department of Computer Engineering, Faculty of Industry and Mining, University of Sistan and Baluchestan, Zahedan, Iran.
تاریخ دریافت: 27 اسفند 1401،  تاریخ بازنگری: 26 اردیبهشت 1402،  تاریخ پذیرش: 24 خرداد 1402 
چکیده
Background and Objectives: In this paper, a new version of the particle swarm optimization (PSO) algorithm using a linear ranking function is proposed for clustering uncertain data. In the proposed Uncertain Particle Swarm Clustering method, called UPSC method, triangular fuzzy numbers (TFNs) are used to represent uncertain data. Triangular fuzzy numbers are a good type of fuzzy numbers and have many applications in the real world.
Methods: In the UPSC method input data are fuzzy numbers. Therefore, to upgrade the standard version of PSO, calculating the distance between the fuzzy numbers is necessary. For this purpose, a linear ranking function is applied in the fitness function of the PSO algorithm to describe the distance between fuzzy vectors.
Results: The performance of the UPSC is tested on six artificial and nine benchmark datasets. The features of these datasets are represented by TFNs.
Conclusion: The experimental results on fuzzy artificial datasets show that the proposed clustering method (UPSC) can cluster fuzzy datasets like or superior to other standard uncertain data clustering methods such as Uncertain K-Means Clustering (UK-means) and Uncertain K-Medoids Clustering (UK-medoids) algorithms. Also, the experimental results on fuzzy benchmark datasets demonstrate that in all datasets except Libras, the UPSC method provides better results in accuracy when compared to other methods. For example, in iris data, the clustering accuracy has increased by 2.67% compared to the UK-means method. In the case of wine data, the accuracy increased with the UPSC method is 1.69%. As another example, it can be said that the increase in accuracy for abalone data was 4%. Comparing the results with the rand index (RI) also shows the superiority of the proposed clustering method.
کلیدواژه‌ها
Heuristic clustering؛ Particle swarm optimization؛ Uncertain data؛ Fuzzy dataset؛ Ranking function
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