基于密度峰值的海量云数据模糊聚类算法设计  

Design of fuzzy clustering algorithm for massive cloud data based on density peak

作　　者：张西广 张龙飞[2] 马钰锡 樊银亭 ZHANG Xi-guang;ZHANG Long-fei;MA Yu-xi;FAN Yin-ting(Zhongyuan-Petersburg Aviation College,Zhongyuan University of Technology,Zhengzhou 450007,China;School of Computer Science&Technology,Beijing Institute of Technology,Beijing 100081,China;Integration&Innovation Center,Institute of Software Chinese Academy of Sciences,Beijing 100080,China)

机构地区：[1]中原工学院中原彼得堡航空学院,郑州450007 [2]北京理工大学计算机学院,北京100081 [3]中国科学院软件研究所集成创新中心,北京100080

出　　处：《吉林大学学报（工学版）》2024年第5期1401-1406,共6页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家重点研发计划项目(2018YFB1403905)。

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。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.

关 键 词：密度峰值 海量云数据 模糊聚类 蝙蝠算法 神经网络 奇异值 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]