K-Means Algorithm Based on Particle Swarm Optimization
學年 92
學期 1
發表日期 2003-12-14
作品名稱 K-Means Algorithm Based on Particle Swarm Optimization
作品名稱（其他語言）
著者 Chen, Ching-Yi; Ye, Fun
作品所屬單位 淡江大學電機工程學系
出版者
會議名稱 2003 International Conference on Informatics, Cybernetics, and Systems
會議地點 高雄, 臺灣
摘要 Clustering analysis aims at discovering groups and identifying interesting distributions and patterns in data sets. It can help the user to distinguish the structure of data and simplify the complexity of data from mass information. A particle swarm optimization-based clustering technique that utilized the principles of K-means algorithm, called KPSO-clustering, is proposed in this article. We attempt to integrate the effectiveness of the K-means algorithm for partitioning data into a number of clusters, with the capability of PSO to bring it out of the local minima. Finally, the effectiveness of the KPSO-clustering is demonstrated on four artificial data sets.
關鍵字 Clustering analysis;Particle swarm optimization;K-means
語言 en
收錄於
會議性質 國際
校內研討會地點
研討會時間 20031214~20031216
通訊作者
國別 TWN
公開徵稿 Y
出版型式 紙本
出處 Proceedings of 2003 International Conference on Informations, Cybernetics, and Systems，頁1470-1475
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