|تعداد مشاهده مقاله||2,426,076|
|تعداد دریافت فایل اصل مقاله||1,711,183|
|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|
|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|
|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.
|Heuristic clustering؛ Particle swarm optimization؛ Uncertain data؛ Fuzzy dataset؛ Ranking function|
 A. Dutt, M. A. Ismail, T. Herawan, “A systematic review on educational data mining,” IEEE Access, 5: 15991-16005, 2017.
 Z. Wang “Determining the clustering centers by slope difference distribution,” IEEE Access, 5: 10995-11002, 2017.
 T. T. Zhang, B. Yuan, “Density-based multiscale analysis for clustering in strong noise settings with varying densities,” IEEE Access, 6: 25861-25873, 2018.
 H. Shahraki, S. H. Zahiri, “Classification of trapezoidal fuzzy data based on heuristic classifiers,” Kasmera, 43(1): 128-144, 2015.
 F. Gullo, “An information-theoretic approach to hierarchical clustering of uncertain data,” Inf. Sci., 402: 199-215, 2017.
 Y. Mao, Y. Liu, M.A. Khan, J. Wang, D. Mao, J. Hu, “Uncertain interval data EFCM-ID clustering algorithm based on machine learning,” J. Rob. Mechatron., 31(2): 339-347, 2019.
 L. Yue, L. Zitu, L. Shuang, G. Yike, L. Qun, W. Guoyin, “Cloud-Cluster: An uncertainty clustering algorithm based on cloud model,” Knowledge-Based Syst., 263, 2023.
 G. S. Nijaguna, K. Thippeswamy, “Multiple kernel fuzzy clustering for uncertain data classification,” Int. J. Comput. Eng. Technol. (IJCET), 10(01): 253-261, 2019.
 C. Ko, J. Baek, B. Tavakkol, Y. S. Jeong, “Cluster Validity Index for Uncertain Data Based on a Probabilistic Distance Measure in Feature Space,” Sensors, 23(7): 3708, 2023.
 B. Tavakkol, Y. Son, “Fuzzy kernel K-medoids clustering algorithm for uncertain data objects,” Pattern Anal. Appl., 24(3): 1287-1302, 2021.
 J. Zhou, L. Chen, C. L. Philip Chen, Y. Wang, H. X. Li, “Uncertain data clustering in distributed peer-to-peer networks,” IEEE Trans. Neural Networks Learn.Syst., 29(6): 2392-2406, 2018.
 H. Shahraki, S. H. Zahiri, “Fuzzy decision function estimation using fuzzified particle swarm optimization,” Int. J. Mach. Learn. Cybern., 8: 1827-1838, 2017.
 R. M.C.R. de Souza, F. de A.T. de Carvalho, “Clustering of interval data based on city–block distances,” Pattern Recognit. Lett., 25: 353–365, 2004.
 F. de A.T. de Carvalho, R. M.C.R. de Souza, M. Chavent, Y. Lechevallier, “Adaptive hausdorff distances and dynamic clustering of symbolic interval data,” Pattern Recognit. Lett., 27: 167–179, 2006.
 X. Zhang, H. Liu, X. Zhang, “Novel density-based and hierarchical density-based clustering algorithms for uncertain data,” Neural Networks, 93: 240–255, 2017.
 J. Tayyebi, E. Hosseinzadeh, “A fuzzy c-means algorithm for clustering fuzzy data and its application in clustering incomplete data,” J. AI Data min., 8(4): 515-523, 2021.
 R. Adrian, S. Sulistyo, I.W. Mustika, S. Alam, “ABNC: Adaptive border node clustering using genes fusion based on genetic algorithm to support the stability of cluster in VANET,” Int. J. Intell. Eng. Syst., 13(1): 354-363, 2020.
 T. P. Q. Nguyen, R. J. Kuo, “Partition-and-merge based fuzzy genetic clustering algorithm for categorical data,” Appl. Soft Comput. J., 75: 254–264, 2019.
 I. Behravan, S. H. Zahiri, S. M. Razavi, R. Trasarti, “Finding roles of players in football using automatic particle swarm optimization-clustering algorithm,” Big Data, 7(1): 35-56, 2019.
 Z. Liu, B. Xiang, Y. Song, H. Lu, Q. Liu, “An improved unsupervised image segmentation method based on multi-objective particle swarm optimization clustering algorithm,” CMC-Comput. Mater. Continua, 58(2): 451-461, 2019.
 B. Anari, J. Akbari torkestani, A. M. Rahmani, “A learning automata‐based clustering algorithm using ant swarm intelligence,” Expert Syst., 35(6): e12310, 2018.
 M. S. Tomar, P. K. Shukla, “Energy efficient gravitational search algorithm and fuzzy based clustering with hop count-based routing for wireless sensor network,” Multimedia Tools Appl., 78: 27849–27870, 2019.
 H. Mittal, M. Saraswat, “An automatic nuclei segmentation method using intelligent gravitational search algorithm based super pixel clustering,” Swarm Evol. Comput., 18(9): S2210-6502, 2018.
 J. Kennedy, R. C. Eberhart, “Particle swarm optimization,” in Proc. IEEE Internal Conference on Neural Networks, 4: 1942-1948, 1995.
 W. Xiong, “Initial clustering based on the swarm intelligence algorithm for computing a data density parameter,” Comput. Intell. Neurosci.: 1-8, 2022.
 D. W. Van der Merwe, A. P. Engelbrecht, “Data clustering using particle swarm optimization in evolutionary computation,” in Proc. The 2003 Congress on Evolutionary Computation, 2003. CEC '03, 2003.
 R. E. Bellman, L. A. Zadeh, “Decision making in a fuzzy environment,” Manag. Sci., 17: 141-164, 1970.
 H. Tanaka, H. Ichihashi, “A formulation of fuzzy linear programming problem based on comparison of fuzzy numbers,” Control Cyber., 13: 185-194, 1984.
 Y.J. Lai, C.L. Hwang, “Fuzzy mathematical programming methods and applications,” Springer, Berlin, 1992.
 S. C. Fang, C. F. Hu, H. F. Wang, S. Y. Wu, “Linear programming with fuzzy coefficients in constraints,” Comput. Math. Appl., 37(10): 63-76, 1999.
 T. Shaocheng, “Interval number and fuzzy number linear programming,” Fuzzy Sets Syst., 66(3): 301-306, 1994.
 C. Garcia-Aguado, J. L. Verdegay, “On the sensitivity of membership functions for fuzzy linear programming problems,” Fuzzy Sets Syst., 56(1): 47-49, 1993.
 H. R. Maleki, “Ranking functions and their applications to fuzzy linear programming,” Far East J. Math. Sci, 4(3): 283-301, 2002.
 Y. L. P. Thorani, P. Phani Bushan Rao, N. Ravi Shankar, “Ordering generalized trapezoidal fuzzy numbers,” Int. J. Contemp. Math. Scie., 7(12): 555 – 573, 2012.
 M. J. Ebadi, M. Suleiman, F. B. Ismail, A. Ahmadian, M. R. Baluch Shahryari, S. Salahshour, “A new distance measure for trapezoidal fuzzy numbers,” Math. Probl. Eng., Article ID: 424186, 2013.
 T. Allahviranloo, M. A. Jahantigh, S. Hajighasemi, “A new distance measure and ranking method for generalized trapezoidal fuzzy numbers,” Math. Probl. Eng., Article ID: 623757, 2013.
 N. Mahdavi-Amiri, S. H. Nasseri, “Duality in fuzzy number linear programming by use of a certain linear ranking function,” Appl. Math. Comput., 180: 206-216, 2006.
 P. K. De. Debaroti Das, “Ranking of trapezoidal intuitionistic fuzzy numbers,” in Proc. 12th International Conference on Intelligent Systems Design and Applications (ISDA), 2012.
 T. Hasuike, “Technical and cost efficiencies with ranking function in fuzzy data envelopment analysis,” in Proc. Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011.
 D. Ponnialagan, J. Selvaraj, L. G. N. Velu, “A complete ranking of trapezoidal fuzzy numbers and its applications to multi-criteria decision making,” Neural Comput. Appl., 30: 3303-3315, 2018.
 R. R. Yager, “A procedure for ordering fuzzy sets of the unit interval,” Inf. Sci., 24: 143-161, 1981.
 H. Shahraki, S. H. Zahiri, “Design and simulation of an RF MEMS switch for removing the self: actuation and latching phenomena using PSO method,” Iran J. Electr. Comput. Eng., 12: 56-63, 2013.
 D. W. van der Merwe, A. P. Engelbrecht, “Data clustering using particle swarm optimization,” in Proc. The 2003 Congress on Evolutionary Computation, 2003. CEC '03: 215-220, 2003.
تعداد مشاهده مقاله: 70
تعداد دریافت فایل اصل مقاله: 67