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Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2017.11.014
Abstract: Abstract In the era of big data, it is increasingly common that large amount of data is generated across multiple distributed sites and cannot be gathered into a centralized site for further analysis, which invalidates…
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Keywords:
efficient distributed;
information;
using boundary;
dcubi ... See more keywords
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Published in 2019 at "International Journal of Control"
DOI: 10.1080/00207179.2017.1378441
Abstract: ABSTRACT This paper presents two dynamic and distributed clustering algorithms for Wireless Sensor Networks (WSNs). Clustering approaches are used in WSNs to improve the network lifetime and scalability by balancing the workload among the clusters.…
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Keywords:
distributed clustering;
voronoi tessellation;
sensor networks;
dynamic distributed ... See more keywords
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Published in 2022 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2022.3177242
Abstract: Distributed clustering based on the Gaussian mixture model (GMM) has exhibited excellent clustering capabilities in peer-to-peer (P2P) networks. However, more iterative numbers and communication overhead are required to achieve the consensus in existing distributed GMM…
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Keywords:
transfer;
distributed clustering;
transfer learning;
mixture model ... See more keywords
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Published in 2021 at "Data"
DOI: 10.3390/data6070073
Abstract: Distributed clustering algorithms have proven to be effective in dramatically reducing execution time. However, distributed environments are characterized by a high rate of failure. Nodes can easily become unreachable. Furthermore, it is not guaranteed that…
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Keywords:
means algorithm;
robust distributed;
distributed clustering;
commodity machines ... See more keywords