SIA OpenIR  > 数字工厂研究室
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
Department数字工厂研究室
Conference NameInternational Conference on Intelligent Computing, Communication and Devices, ICCD 2017
Conference DateDecember 9-10, 2017
Conference PlaceShenzhen, China
Source PublicationAdvances in Intelligent Systems and Computing, Recent Developments in Intelligent Computing, Communication and Devices - Proceedings of ICCD 2017
PublisherSpringer Verlag
Publication PlaceBerlin
2017
Pages349-356
Indexed ByEI
EI Accession number20183805830473
Contribution Rank1
ISSN2194-5357
ISBN978-981-10-8943-5
KeywordK-mcdoids Gray Incidcnce Clustering Algorithm Aluminum Electrolysis
Abstract

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.

Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/22732
Collection数字工厂研究室
Corresponding AuthorGao SY(高诗莹)
Affiliation1.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
Recommended Citation
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.
Files in This Item:
File Name/Size DocType Version Access License
Improved K-medoids c(319KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Gao SY(高诗莹)]'s Articles
[Zhou XF(周晓锋)]'s Articles
[Li S(李帅)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Gao SY(高诗莹)]'s Articles
[Zhou XF(周晓锋)]'s Articles
[Li S(李帅)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Gao SY(高诗莹)]'s Articles
[Zhou XF(周晓锋)]'s Articles
[Li S(李帅)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Improved K-medoids clustering based on gray association rule.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.