DBSCAN behaviour analysis and N-Adaboost prediction model research for mathematics majors' academic prediction Online publication date: Fri, 13-Sep-2024
by Xiaoni Zhang
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 27, No. 3, 2024
Abstract: From the perspective of distance optimisation, a density-based spatial clustering applied noise algorithm is proposed for clustering analysis and academic prediction of mathematical student behaviour. This algorithm improves the clustering effect and accuracy by improving the selection of neighbourhood radius. Secondly, to address the limited learning performance of a single classifier, an N-Adaboost model based on multiple classifiers is proposed. The experiment shows that when the number of clusters is 4, the network behaviour description index reaches the optimal level, with a maximum contour coefficient of 0.667. The N-Adaboost prediction model has high accuracy, accuracy and recall rate. When N=3, the model has the best performance and can successfully predict and analyse data. In summary, the density based noisy clustering algorithm based on distance optimisation and the N-Adaboost prediction model based on multiple classifiers have broad application prospects in student behaviour clustering analysis and academic prediction problems.
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