Research on spectral clustering algorithm for network communication big data based on wavelet analysis
by Xinjian Dai; Zhichao Zeng
International Journal of Autonomous and Adaptive Communications Systems (IJAACS), Vol. 15, No. 2, 2022

Abstract: In order to classify the features of big data in network communication, improve clustering efficiency and reduce error classification rate, a spectrum clustering algorithm based on wavelet analysis is proposed. The multi-scale, one-dimensional wavelet analysis method is used to sample the network communication big data, extract the spectral feature quantity of the network communication big data, and construct the channel model of big data transmission of network communication. Combined with the fuzzy C-means clustering method, the spectral clustering is performed on network communication big data to mine association rules of the large data spectrum of network communication. Combined with wavelet decomposition method, the time-frequency conversion and feature separation of network communication big data spectrum are carried out to complete the spectrum clustering of network communication big data. Simulation results show that this method is more accurate for spectrum clustering of communication big data and improves clustering efficiency.

Online publication date: Tue, 21-Jun-2022

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