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An optimized initialization center K-means clustering algorithm based on density
Yuan QL(袁启龙); Shi HB(史海波); Zhou XF(周晓锋)
作者部门数字工厂研究室
会议名称2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
会议日期June 8-12, 2015
会议地点Shenyang, China
会议录名称2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
出版者IEEE
出版地Piscataway, NJ, USA
2015
页码790-794
收录类别EI ; CPCI(ISTP)
EI收录号20161402187785
WOS记录号WOS:000380502300150
产权排序1
ISSN号2379-7711
ISBN号978-1-4799-8730-6
关键词Clustering K-means Algorithm Initial Center Points Neighborhood Density Distance
摘要Traditional K-means algorithm's clustering effect is affected by the initial cluster center points. To solve this problem, a method is proposed to optimize the K-means initial center points. The algorithm use density-sensitive similarity measure to compute the density of objects. Through computing the minimum distance between the point and any other point with higher density, the candidate points are chosen out. Then, combined with the average density, the outliers are screened out. Ultimately the initial centers for K-means algorithm are screened out. Experimental results show that the algorithm gets the initial center points with high accuracy, and can effectively filter abnormal points. The running time and the iterations of the K-means algorithm are decreased obviously.
语种英语
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被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/18522
专题数字工厂研究室
作者单位1.University of Chinese Academy of Sciences, Wuxi CAS Ubiquitous Technology RandD Center CO. LTD., Beijing, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
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Yuan QL,Shi HB,Zhou XF. An optimized initialization center K-means clustering algorithm based on density[C]. Piscataway, NJ, USA:IEEE,2015:790-794.
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