關鍵字查詢 | 類別:會議論文 | | 關鍵字:Particle Swarm Optimization Algorithm and Its Application to Clustering Analysis

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序號 學年期 教師動態
1 92/2 電機系 余 繁 教授 會議論文 發佈 Particle Swarm Optimization Algorithm and Its Application to Clustering Analysis , [92-2] :Particle Swarm Optimization Algorithm and Its Application to Clustering Analysis會議論文Particle Swarm Optimization Algorithm and Its Application to Clustering AnalysisChen, Ching-Yi; Ye, Fun淡江大學電機工程學系Clustering analysis;PSOPiscataway: Institute of Electrical and Electronics Engineers (IEEE)Networking, Sensing and Control, 2004 IEEE International Conference on, vol.2, pp.789-794IEEE Systems, Man and Cybernetics SocietyClustering analysis is applied generally to Pattern Recognition, Color Quantization and Image Classification. It can help the user to distinguish the structure of data and simplify the complexity of data from mass information. The user can understand the implied information behind extracting these data. In real case, the distribution of information can be any size and shape. A particle swarm optimization algorithm-based technique, called PSO-clustering, is proposed in this article. We adopt the particle swarm optimization to search the cluster center in the arbitrary data set
2 92/1 電機系 余 繁 教授 會議論文 發佈 Particle swarm optimization algorithm and its application to clustering analysis , [92-1] :Particle swarm optimization algorithm and its application to clustering analysis會議論文Particle swarm optimization algorithm and its application to clustering analysisChen, Ching-yi; Ye, Fun淡江大學電機工程學系N.Y.: IEEE (Institute of Electrical and Electronic Engineers)2004 IEEE International Conference on Networking, Sensing and Control, 2, pp.789-794Clustering analysis is applied generally to pattern recognition, color quantization and image classification. It can help the user to distinguish the structure of data and simplify the complexity of data from mass information. The user can understand the implied information behind extracting these data. In real case, the distribution of information can be any size and shape. A particle swarm optimization algorithm-based technique, called PSO-clustering, is proposed in this article. We adopt the particle swarm optimization to search the cluster center in the arbitrary data set automatically. PSO can search the best solution from the probability option
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