Title: Online education big data mining method based on association rules

Authors: Na Zhang

Addresses: AI College, Henan Finance University, Zhengzhou 450046, Henan, China

Abstract: In order to solve the problems of slow mining speed, high noise and poor data correlation in the existing online education big data mining methods, an online education big data mining method based on association rules is designed. Firstly, the recursive distance of the big data centre is determined, and the online education big data is extracted according to the calculation of fuzzy membership. Secondly, the covariance matrix is used to remove the noise in online education big data and reduce the dimension. Finally, the confidence and support of online education big data association rules are calculated, the association strength between online education big data in the set is determined, and the data mining is completed. The experimental results show that the mining speed of this method is significantly improved, the longest time is no more than 4 s, and the data mining is highly correlated.

Keywords: association rules; online education; big data; Euclidean distance; uniformity.

DOI: 10.1504/IJICT.2024.137931

International Journal of Information and Communication Technology, 2024 Vol.24 No.3, pp.262 - 272

Received: 09 Dec 2021
Accepted: 14 Jan 2022
Published online: 11 Apr 2024 *

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