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首页> 外文期刊>International journal of autonomous and adaptive communications systems >Data stream mining for wireless sensor networks environment: energy efficient fuzzy clustering algorithm
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Data stream mining for wireless sensor networks environment: energy efficient fuzzy clustering algorithm

机译:无线传感器网络环境下的数据流挖掘:节能模糊聚类算法

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This paper proposes a distributed wireless sensor network data stream clustering algorithm to minimise energy consumption and consequently extend the network lifetime. The efficiency in energy usage is as a result of trading-off communication for computation through distributed clustering and successive transmission of local clusters. We present the development of our algorithm, subtractive fuzzy cluster means (SUBFCM), and analyse its energy efficiency as well as clustering performance in comparison with state-of-the-art standard data clustering algorithms such as fuzzy C-means and K-means algorithms. The significance of the SUBFCM algorithm in terms of energy efficiency and clustering performance is proved through simulations as well as experiments.
机译:本文提出了一种分布式无线传感器网络数据流聚类算法,以最小化能耗,从而延长网络寿命。能源使用效率的高低是通过分布式集群和连续传输本地集群进行权衡通信以进行计算的结果。我们介绍了我们的算法减法模糊聚类均值(SUBFCM)的开发,并与诸如模糊C均值和K均值的最新标准数据聚类算法进行了比较,分析了其能效以及聚类性能算法。通过仿真和实验证明了SUBFCM算法在能源效率和聚类性能方面的重要性。

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