Title: A method for merging and classifying higher mathematics teaching resources based on density clustering algorithm

Authors: Hejie Chang; Xing Lv

Addresses: Luohe Institute of Technology, Henan University of Technology, Luohe, 462000, China ' Luohe Institute of Technology, Henan University of Technology, Luohe, 462000, China

Abstract: To enhance the recall and accuracy of resource merging classification, this study introduces a merging classification technique rooted in density clustering algorithms. Initially, we gather data pertaining to higher mathematics teaching resources. Subsequently, we convert textual sentences into word-level representations, eliminating stop words and unnecessary high-frequency vocabulary. Leveraging LDA, we extract mathematical resource features, transforming words into computer- and model-recognisable vectorised forms. Next, we calculate the density and distance between samples to categorise them into distinct groups, employing density clustering algorithms for merging and classifying teaching resources. Experimental findings reveal that our method achieves a classification recall rate of 99.6% and an accuracy of 99.9%, thereby enhancing the quality and efficacy of higher mathematics education.

Keywords: density clustering; merge and classify; advanced mathematics; teaching resources; resource allocation.

DOI: 10.1504/IJBIDM.2025.145359

International Journal of Business Intelligence and Data Mining, 2025 Vol.26 No.3/4, pp.382 - 395

Received: 27 Dec 2023
Accepted: 03 Aug 2024
Published online: 31 Mar 2025 *

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