caa a22 4500 605470588 CHVBK 20210128100328.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00500-014-1255-3 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1255-3 A fast particle swarm optimization for clustering [Elektronische Daten] [Chun-Wei Tsai, Ko-Wei Huang, Chu-Sing Yang, Ming-Chao Chiang] This paper presents a high-performance method to reduce the time complexity of particle swarm optimization (PSO) and its variants in solving the partitional clustering problem. The proposed method works by adding two additional operators to the PSO-based algorithms. The pattern reduction operator is aimed to reduce the computation time, by compressing at each iteration patterns that are unlikely to change the clusters to which they belong thereafter while the multistart operator is aimed to improve the quality of the clustering result, by enforcing the diversity of the population to prevent the proposed method from getting stuck in local optima. To evaluate the performance of the proposed method, we compare it with several state-of-the-art PSO-based methods in solving data clustering, image clustering, and codebook generation problems. Our simulation results indicate that not only can the proposed method significantly reduce the computation time of PSO-based algorithms, but it can also provide a clustering result that matches or outperforms the result PSO-based algorithms by themselves can provide. Springer-Verlag Berlin Heidelberg, 2014 Clustering nationallicence Particle swarm optimization nationallicence Pattern reduction nationallicence Tsai Chun-Wei Department of Applied Informatics and Multimedia, Chia Nan University of Pharmacy and Science, 71710, Tainan, Taiwan, R.O.C. aut Huang Ko-Wei Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, 70101, Tainan, Taiwan, R.O.C. aut Yang Chu-Sing Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, 70101, Tainan, Taiwan, R.O.C. aut Chiang Ming-Chao Department of Computer Science and Engineering, National Sun Yat-sen University, 80424, Kaohsiung, Taiwan, R.O.C. aut Soft Computing Springer Berlin Heidelberg 19/2(2015-02-01), 321-338 1432-7643 19:2<321 2015 19 500 https://doi.org/10.1007/s00500-014-1255-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1255-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Tsai Chun-Wei Department of Applied Informatics and Multimedia, Chia Nan University of Pharmacy and Science, 71710, Tainan, Taiwan, R.O.C aut NATIONALLICENCE 700 1- Huang Ko-Wei Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, 70101, Tainan, Taiwan, R.O.C aut NATIONALLICENCE 700 1- Yang Chu-Sing Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, 70101, Tainan, Taiwan, R.O.C aut NATIONALLICENCE 700 1- Chiang Ming-Chao Department of Computer Science and Engineering, National Sun Yat-sen University, 80424, Kaohsiung, Taiwan, R.O.C aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/2(2015-02-01), 321-338 1432-7643 19:2<321 2015 19 500