Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (5): 1401-1406.doi: 10.13229/j.cnki.jdxbgxb.20230120

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

Xi-guang ZHANG1(),Long-fei ZHANG2,Yu-xi MA3,Yin-ting FAN1   

  1. 1.Zhongyuan-Petersburg Aviation College，Zhongyuan University of Technology，Zhengzhou 450007，China
    2.School of Computer Science & Technology，Beijing Institute of Technology，Beijing 100081，China
    3.Integration & Innovation Center，Institute of Software Chinese Academy of Sciences，Beijing 100080，China
  • Received:2023-02-09 Online:2024-05-01 Published:2024-06-11

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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.

Key words: peak density, massive cloud data, fuzzy clustering, bat algorithm, neural network, singular value

CLC Number: 

  • TP391

Fig.1

Structure of BP neural network"

Fig.2

Operation flow chart of fuzzy clustering algorithm for massive cloud data based on density peak"

Fig.3

Distribution of test data set"

Fig.4

Comparative analysis of cloud data fuzzy clustering results of different algorithms"

Fig.5

Comparative analysis of clustering purity test results of different algorithms"

Fig.6

Comparison of clustering efficiency results of different algorithms under different data sets"

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