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Lin Peng; Wang Yinghui; Qi Hongsheng; Hong Yiguang
Source Publicationjournalofsystemsscienceandcomplexity
AbstractThis paper discusses a distributed design for clustering based on the K-means algorithm in a switching multi-agent network, for the case when data are decentralized stored and unavailable to all agents. The authors propose a consensus-based algorithm in distributed case, that is, the double- clock consensus-based K-means algorithm (DCKA). With mild connectivity conditions, the authors show convergence of DCKA to guarantee a distributed solution to the clustering problem, even though the network topology is time-varying. Moreover, the authors provide experimental results on vari- ous clustering datasets to illustrate the effectiveness of the fully distributed algorithm DCKA, whose performance may be better than that of the centralized K-means algorithm.
Document Type期刊论文
Recommended Citation
GB/T 7714
Lin Peng,Wang Yinghui,Qi Hongsheng,et al. distributedconsensusbasedkmeansalgorithminswitchingmultiagentnetworks[J]. journalofsystemsscienceandcomplexity,2018,031(005):1128.
APA Lin Peng,Wang Yinghui,Qi Hongsheng,&Hong Yiguang.(2018).distributedconsensusbasedkmeansalgorithminswitchingmultiagentnetworks.journalofsystemsscienceandcomplexity,031(005),1128.
MLA Lin Peng,et al."distributedconsensusbasedkmeansalgorithminswitchingmultiagentnetworks".journalofsystemsscienceandcomplexity 031.005(2018):1128.
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