Name
Abstract | ||
|---|---|---|
This 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 various 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. |
Year | Venue | Field |
|---|---|---|
2018 | J. Systems Science & Complexity | Convergence (routing),Consensus,k-means clustering,Mathematical optimization,Network topology,Distributed algorithm,Cluster analysis,Mathematics |
DocType | Volume | Issue |
Journal | 31 | 5 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| peng lin | 1 | 39 | 12.10 |
| Yinghui Wang | 2 | 60 | 16.82 |
| hongsheng qi | 3 | 710 | 46.37 |
| Yiguang Hong | 4 | 3274 | 217.75 |