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Using Apriori Algorithm on Students' Performance Data for Association Rules Mining

机译:使用APRIORI算法对学生的关联规则挖掘性能数据

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With the development of information technology, many colleges and universities have established student information management system. The long-term operation of the student information management system will generate big data for colleges and universities. Moreover, there exists valuable information in the huge amount of data. Hence, it is necessary to use the data mining method to mine the massive data and get some valuable reference information so as to improve the teaching and management of students. In this paper, the Apriori algorithm is used to mine association rules of 34 courses of 100 students majoring in computer science and technology, so as to find out the correlation between courses and the factors that lead to the high or low grades of courses. R is used to conduct the experiment to discover the association rules, and the association rules are analyzed and discussed. The results of data mining on students' achievements in this work are expected to provide a reference for improving the teaching quality of computer science and technology courses.
机译:随着信息技术的发展,许多高校建立了学生信息管理系统。学生信息管理系统的长期运行会产生对高校的大数据。此外,存在着巨大的数据量有价值的信息。因此,有必要使用数据挖掘方法开采了大量的数据,并得到一些有价值的参考信息,从而提高学生的教学和管理。在本文中,先验算法来的100名学生在计算机科学与技术专业的34门课程关联规则挖掘,以找出课程和各因素之间的相关性是导致高或低年级的课程。 R用于进行该实验来发现关联规则,和关联规则进行了分析和讨论。对学生在这项工作中取得的成就数据挖掘的结果有望为提高计算机科学与技术课程教学质量的参考。

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