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Distributed Consensus-Based K-Means Algorithm in Switching Multi-Agent Networks

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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.

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Authors and Affiliations

  1. Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Peng Lin, Yinghui Wang, Hongsheng Qi & Yiguang Hong

  2. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China

    Peng Lin, Yinghui Wang, Hongsheng Qi & Yiguang Hong

Authors
  1. Peng Lin
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  2. Yinghui Wang
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  3. Hongsheng Qi
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  4. Yiguang Hong
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Corresponding author

Correspondence to Peng Lin.

Additional information

This research was supported by the National Key Research and Development Program of China under Grant No. 2016YFB0901902 and the National Natural Science Foundation of China under Grant Nos. 61573344, 61333001, 61733018, and 61374168.

This paper was recommended for publication by Editor HU Xiaoming.

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Lin, P., Wang, Y., Qi, H. et al. Distributed Consensus-Based K-Means Algorithm in Switching Multi-Agent Networks. J Syst Sci Complex 31, 1128–1145 (2018). https://doi.org/10.1007/s11424-018-7102-3

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  • DOI: https://doi.org/10.1007/s11424-018-7102-3

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