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Improved K-medoids clustering based on gray association rule
Gao SY(高诗莹)1,3,4; Zhou XF(周晓锋)1,3; Li S(李帅)1,2,3
作者部门数字工厂研究室
会议名称International Conference on Intelligent Computing, Communication and Devices, ICCD 2017
会议日期December 9-10, 2017
会议地点Shenzhen, China
会议录名称Advances in Intelligent Systems and Computing, Recent Developments in Intelligent Computing, Communication and Devices - Proceedings of ICCD 2017
出版者Springer Verlag
出版地Berlin
2017
页码349-356
收录类别EI
EI收录号20183805830473
产权排序1
ISSN号2194-5357
ISBN号978-981-10-8943-5
关键词K-mcdoids Gray Incidcnce Clustering Algorithm Aluminum Electrolysis
摘要

This paper presents a new K-Medoids clustering algorithm based on gray relational degree. Analyze the gray incidence of each attribute and convert them into the weights of the attributes, and then apply these weights to the distance measure of the cluster; based on this measure, this paper proposed an improved clustering algorithm: Gray-K-Medoids clustering algorithm and applied it to the analysis of the aluminum electrolysis data. The paper introduces the gray relational degree and the basic principle based on the gray relational degree clustering and introduced the improved algorithm in detail. In order to test the effect of improving the algorithm, it was used to the production data of an aluminum plant, and the results show the effectiveness of the algorithm, has a certain promotional value.

语种英语
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/22732
专题数字工厂研究室
通讯作者Gao SY(高诗莹)
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang 110016, China
4.School of Computer Science and Engineering, Northeastern University, Shenyang 110000, China
推荐引用方式
GB/T 7714
Gao SY,Zhou XF,Li S. Improved K-medoids clustering based on gray association rule[C]. Berlin:Springer Verlag,2017:349-356.
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