Science Teaching Department, Zhengzhou Preschool Education College, Zhengzhou 450000, China.
Comput Intell Neurosci. 2022 Jul 31;2022:3762431. doi: 10.1155/2022/3762431. eCollection 2022.
It is thought to be an effective technique to handle the problem of educational data explosion and lack of information by identifying potential relationships between data and directing decision-makers through the extraction, transformation, analysis, and modeling of educational data. Based on this, this research constructs a data analysis model for education evaluation using the K-means clustering technique in DM. The weight of each index of students' comprehensive quality is calculated using AHP, and the value of the weight is used to determine whether the index is the important feature of analysis system mining. Improved sampling technology is used to deal with the representation of large-scale data sets; a sample partition clustering technique is proposed as a general framework. The best accuracy of this method, according to experimental data, is 95.6 percent, which is 12.1 percent greater than Mi cluster algorithm and 6.8 percent higher than DRCluster algorithm. The K-means clustering analysis technology is used to analyze students' comprehensive evaluation data in this paper, with the goal of determining the regularity of data implication, accurately diagnosing learning problems, and providing the foundation for developing effective student management strategies.
人们认为，通过识别数据之间的潜在关系，并通过对教育数据进行提取、转换、分析和建模，为决策者提供指导，可以有效解决教育数据爆炸和信息匮乏的问题。基于此，本研究构建了一个基于数据挖掘（DM）中的 K-均值聚类技术的教育评估数据分析模型。使用层次分析法（AHP）计算学生综合素质各指标的权重，并使用权重值来确定该指标是否为分析系统挖掘的重要特征。改进的抽样技术用于处理大规模数据集的表示；提出了一种样本分区聚类技术作为通用框架。根据实验数据，该方法的最佳准确率为 95.6%，比 Mi 聚类算法高 12.1%，比 DRCluster 算法高 6.8%。本文采用 K-均值聚类分析技术对学生的综合评价数据进行分析，旨在确定数据蕴涵的规律，准确诊断学习问题，为制定有效的学生管理策略提供依据。
