最小支持度挖掘算法在高校学生成绩关联规则的应用  

Application of Minimal Support Mining Algorithm in Association Rules of College Students'Grades

作　　者：柯红香 Ke Hongxiang(Zhangzhou Vocational College of Science and Technology,Zhangpu,363202,Fujian,China)

机构地区：[1]漳州科技职业学院,福建漳浦363202

出　　处：《长江工程职业技术学院学报》2023年第2期69-73,共5页Journal of Changjiang Institute of Technology

基　　金：漳州科技职业学院科研课题“基于关联性规则的学生就业数据研究”(项目编号:ZK202006)。

摘　　要：以漳州科技职业学院市场营销专业152名学生47门课程的成绩信息作为研究数据,根据课程成绩分布规律,利用标准差划分等级进行成绩离散化处理,针对传统关联规则Apriori算法单一的最小支持度的局限性,提出了一种自适应多最小支持度关联规则算法,采用统计拟合方法实现最小支持度和最小置信度的自适应取值,并将置信度和提升度相结合的模式筛选出有价值的规则,从而得到市场营销专业不同课程的关联性,为创新人才培养改革提供参考。Taking the grade information of 47 courses of 152 students majoring in marketing in Zhangzhou Vocational College of Science and Technology as the research data,according to the distribution of course grades,using the standard deviation to divide grades for grade discretization,aiming at the single minimum support of the traditional association rule Apriori algorithm In view of the limitations of degrees,an adaptive multi-minimum support association rule algorithm is proposed,and the statistical fitting method is used to realize the self-adaptive value of the minimum support and minimum confidence,and the combination of confidence and promotion is used for pattern screening.Valuable rules are drawn up,so as to obtain the relevance of different courses of marketing majors,and provide a reference for the reform of innovative talent training.

关 键 词：数据挖掘 关联规则 学生成绩 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]