【摘要】 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。更多还原

【Abstract】 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.更多还原