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An Improved CFSFDP Algorithm with Cluster Center Automatically Selected Based on Weighted Average Method
Lv Z(吕哲)1,2; Han ZH(韩忠华)1,3; Yang LY(杨丽英)2; Gang W(宫巍)1
作者部门机器人学研究室
会议名称7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
会议日期July 31 - August 4, 2017
会议地点Hawaii, USA
会议主办者IEEE Robotics and Automation Society
会议录名称2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
出版者IEEE
出版地New York
2017
页码955-959
收录类别EI ; CPCI(ISTP)
EI收录号20183905873443
WOS记录号WOS:000447628700174
产权排序1
ISBN号978-1-5386-0489-2
摘要

Motived by that clustering by fast search and find of density peaks algorithm(CFSFDP) could not select the cluster center automatically, an improved CFSFDP algorithm was presented. The improved method uses the weighted average method and calculates the weighted averages of center weight for each point as the screening indices of cluster center. The data point will be selected as cluster center if its center weight is greater than the calculated weighted average. With aforesaid method, the improved algorithm could select the cluster center automatically, and effectively avoid the err by human made according to the decision graph. Simulation experiments comparing with K-means and CFSFDP on 4 kinds of UCI datasets are shown that the improved algorithm could retain the accurate and fast characteristics of CFSFDP and select the cluster center automatically.

语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/22839
专题机器人学研究室
通讯作者Lv Z(吕哲)
作者单位1.Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
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GB/T 7714
Lv Z,Han ZH,Yang LY,et al. An Improved CFSFDP Algorithm with Cluster Center Automatically Selected Based on Weighted Average Method[C]//IEEE Robotics and Automation Society. New York:IEEE,2017:955-959.
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