Title: Optimisation of massive MIMO data classification algorithm based on fuzzy C-means and differential evolution method

Authors: Jia Chen

Addresses: Puyang Vocational and Technical College, Puyang, Henan 457000, China

Abstract: In the environment of massive data multiple input multiple output (MIMO), it leads to poor data recognisability, clustering ability and overall data processing ability. Therefore, a massive MIMO data classification algorithm based on fuzzy C-means and differential evolution method is proposed. The information flow model of time series of massive data in data flow is established, and the time series features of massive data are extracted by using autocorrelation feature analysis method. Through fuzzy C-means, the features of massive data are cross fused and clustered. The global convergence and stability of data classification are adjusted and controlled by differential evaluation method, and then the delay of data classification is modified to optimise the data classification algorithm. The simulation results show that the data classification recall rate of this method is higher than 95%, and it has the advantages of strong clustering ability and low misclassification rate.

Keywords: fuzzy C-means; differential evolution; data classification; fusion clustering; information fusion.

DOI: 10.1504/IJICT.2021.117045

International Journal of Information and Communication Technology, 2021 Vol.19 No.2, pp.156 - 167

Received: 01 Feb 2020
Accepted: 25 Mar 2020

Published online: 13 Aug 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article