首页|基于密度峰值的海量云数据模糊聚类算法设计

基于密度峰值的海量云数据模糊聚类算法设计

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为准确聚类海量云数据,提出一种基于密度峰值的海量云数据模糊聚类算法.将含有噪声的云数据采用BP神经网络分离,将输出的噪声利用奇异值分解重构,获取联合算法输出的噪声,将带有噪声的云数据和输出噪声相减,得到去噪后的云数据.将密度峰值和优化后的模糊聚类算法相结合,自适应形成初始聚类中心,确定聚类数量,最终实现海量云数据模糊聚类.实验结果表明:本文算法获取的聚类效果和聚类效率明显优于其他算法.
Design of fuzzy clustering algorithm for massive cloud data based on density peak
In order to cluster massive cloud data accurately,a fuzzy clustering algorithm for massive cloud data based on peak density is proposed.The cloud data with noise is separated by BP neural network,and the output noise is reconstructed by singular value decomposition to obtain the noise output by the joint algorithm.The cloud data with noise is subtracted from the output noise to obtain the cloud data after noise removal.The density peak is combined with the optimized fuzzy clustering algorithm to adaptively form the initial clustering center,determine the number of clusters,and finally realize the fuzzy clustering of massive cloud data.Experimental results show that the clustering effect and efficiency of the proposed algorithm are significantly better than other algorithms.

peak densitymassive cloud datafuzzy clusteringbat algorithmneural networksingular value

张西广、张龙飞、马钰锡、樊银亭

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中原工学院 中原彼得堡航空学院,郑州 450007

北京理工大学 计算机学院,北京 100081

中国科学院软件研究所集成创新中心,北京 100080

密度峰值 海量云数据 模糊聚类 蝙蝠算法 神经网络 奇异值

国家重点研发计划

2018YFB1403905

2024

吉林大学学报(工学版)
吉林大学

吉林大学学报(工学版)

CSTPCD北大核心
影响因子:0.792
ISSN:1671-5497
年,卷(期):2024.54(5)
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