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题名: Hybridization of particle swarm optimization with the K-Means algorithm for clustering analysis
作者: Shen H(申海) ; Zhu YL(朱云龙) ; Jin L(金莉) ; Zhu Z(朱珠)
作者部门: 工业信息学研究室
会议名称: 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010
会议日期: September 23-26, 2010
会议地点: Changsha, China
会议主办者: IEEE Beijing Section; Hunan University; Liverpool Hope University; Peking University; National Natural Science Foundation of China
会议录: Proceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010
会议录出版者: IEEE Computer Society
会议录出版地: Piscataway, NJ
出版日期: 2010
页码: 531-535
收录类别: EI
ISBN号: 9781424464388
关键词: Cluster analysis ; Clustering algorithms ; Computation theory ; Convergence of numerical methods ; Function evaluation ; Pattern recognition ; Problem solving
摘要: Clustering is an unsupervised classification technique which deals with pattern recognition problems. While traditional analytical methods suffer from slow convergence and the challenges of high-dimensional. Recent years, particle swarm optimization (PSO) has successfully been applied to a number of real world clustering problems with the fast convergence and the effectively for high-dimensional data. This paper presents a detailed overview of hybrid algorithms combining PSO with K-Means algorithm for solving clustering problem. For each algorithm, technical details that are required for applying clustering, such as its type, particle formulation, and the most efficient fitness functions are also discussed. Finally, a summary is given together with suggestions for future research.
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/8360
Appears in Collections:工业信息学研究室_会议论文

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