Title: Data mining of abnormal behaviour in English learning based on dynamic grid generation algorithm

Authors: Le Yang

Addresses: School of Culture, Tourism and International Education, Henan Polytechnic Institute, Nanyang, 473000, China

Abstract: To address the limitations of traditional data mining methods for detecting abnormal behaviour in English learning - such as low accuracy, low recall rates, and long processing times - this paper proposes a joint data mining method based on a dynamic grid generation algorithm. The proposed approach begins by collecting instances of abnormal behaviour in English learning to construct a behavioural dataset. Next, the RANSAC algorithm eliminates erroneous feature matching points, while the dynamic grid generation algorithm performs data feature matching. Finally, an abnormal behaviour data mining function is constructed to identify anomalies in English learning behaviours. Experimental results demonstrate the method's effectiveness, achieving a recall rate of 96.5%, an accuracy of 99.15%, and a processing time of just six seconds, confirming its efficiency in mining abnormal behaviour data in English learning.

Keywords: feature matching; dynamic grid generation algorithm; data mining; Ransac algorithm.

DOI: 10.1504/IJBIDM.2026.151266

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.1, pp.41 - 59

Accepted: 05 Jun 2025
Published online: 20 Jan 2026 *

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