课程推荐系统中的增强Apriori算法模型

2015 International Conference and Workshop on Computing and Communication (IEMCON) Pub Date : 2015-10-01 DOI:10.1109/IEMCON.2015.7344527

Pawan S. Bhandari, Chandana Withana, A. Alsadoon, A. Elchouemi

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引用次数: 4

摘要

关联规则挖掘(ARM)中包含了一种最流行的算法——Apriori算法(AA)。AA在执行时间消耗方面有一些限制和需要改进的地方。扩展AA算法在现有AA算法的基础上进行了改进。主要发现表明，考虑到观察到的数据量，在教育数据挖掘方面做得并不多。因此，本文旨在评估ARM在教育数据挖掘环境中的应用程度。具体而言，本文以学生课程规划系统为研究对象，开发了一种扩展的AA挖掘算法并将其应用于高等教育系统。本项目重点介绍了AA和ARM，实现了AA在教育数据中的增强功能，开发了课程推荐模型。最后，将改进后的算法与现有的AA进行比较，以帮助学生构建课程建议系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Enhanced Apriori Algorithm model in course suggestion system

Association Rule Mining (ARM) includes one of the most popular algorithms called Apriori Algorithm (AA) in it. AA has some limitations and areas for improvement related to the execution time consumption. The extended AA has some improvements based on the existing algorithm AA. The main findings indicate that not much has been done in an educational data mining considering the volume of data that is observed. Therefore, it is intended to evaluate to what extent an ARM can be utilized in an educational data mining context. Specifically, this paper develops an extended AA mining algorithm and applies it to the higher education system, focusing on the student's course planning system. This project focuses on introducing AA and ARM, implementing the enhanced features of the AA in educational data, develop the course recommender model. Finally, it evaluates the enhanced algorithm compared to the existing AA to help build a course suggestion system for students.

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2015 International Conference and Workshop on Computing and Communication (IEMCON)

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