Title: Data mining method for English distance learning based on weighted fast clustering

Authors: Xiaohong Yu

Addresses: College of Applied Foreign Languages, Eastern Liaoning University, Dandong, 118001, Liaoning, China

Abstract: In order to improve the efficiency of English distance teaching data mining, mining recall and accuracy, a weighted fast clustering based English distance teaching data mining method is proposed. Firstly, the data of English distance teaching are collected from three dimensions: students' basic characteristics, online teaching behaviour characteristics, and students' learning effect characteristics. Secondly, data preprocessing is realised through data integration, data selection, data cleaning, attribute construction and other processes. Finally, based on the weighted depth forest, calculate the attribute weight of the data, calculate the similarity between the data through the weighted fast clustering method, determine the English distance teaching data mining list through the similarity between teaching data, and realise the English distance teaching data mining. The experimental results show that the mining accuracy and recall of this method are high, and the mining time is short, indicating that the data mining effect of this method is good.

Keywords: data cleaning; data preprocessing; weighted depth forest; data mining; weighted fast clustering.

DOI: 10.1504/IJRIS.2024.143160

International Journal of Reasoning-based Intelligent Systems, 2024 Vol.16 No.5, pp.367 - 374

Received: 16 Jan 2023
Accepted: 21 Mar 2023
Published online: 05 Dec 2024 *

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