基于频繁项集的学生选课行为分析

Analysis of students ' elective course behavior based on frequent itemsets

在大数据时代,数据分析深度融合到各行各业中,教育作为数据挖掘逐步涉及的领域,许多技术得到了快速的发展。当今大部分高校对学生行为的分析存在着较大的盲区,本文依据教育数据挖掘技术,通过关联规则挖掘,对学生课程选择的最小关联规则进行挖掘,以此进行学生的行为分析。基于APRIOR算法发现并生成频繁项集,从中挖掘出同时满足最小支持度和最小置信度的强关联规则,并建立学生选课关联特征模型,分析其中的特殊联系及潜在规律。最后,通过实验验证该算法具有实际意义,对提高学校的管理和教学以及对学生更好的认识自身提供帮助。

In the Big data age, the data analysis is deeply integrated into all walks of life, education as data mining gradually involved in the field, many technologies have been rapid development.Nowadays, most colleges and universities have a big blind spot in the analysis of students ＇ behavior, based on the education data mining technology, this paper excavates the minimum Association rules of Students ＇ course selection by mining the association rules, in order to analyze the students ＇ behavior.Based on the Aprior algorithm, the frequent itemsets are discovered and generated, and the strong association rules satisfying the minimum support and the minimum confidence are excavated, and the characteristic model of the students ＇ elective course is established, and the special relationship and the potential rules are analyzed.Finally, the experiment verifies that the algorithm has practical significance, and it can help to improve the management and teaching of the school and the students ＇ better understanding.