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基于改进Apriori算法的考试成绩分析 被引量:1

An Analysis of Examination Achievement Based on the Improved Apriori Algorithm
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摘要 为了对学生考试成绩数据进行分析,提出一种改进的Apriori算法。在垂直数据表示方式上采用广度优先搜索和交叉计数,充分发挥垂直数据表示与交叉技术的效率优势,同时利用Apriori算法的剪枝策略,有效减少计数后选项集的数目。最后将改进后Apriori算法应用于考试成绩分析,实验结果发现学生各科目考试成绩优良影响关系,为学生学习提供引导,为教师教学提供参考。 In order to analyze examination achievement,an improved algorithm is given based on Apriori algorithm in this paper.The new algorithm is implemented with vertical data layout,breadth first searching,and intersecting.It takes advantage of the efficiency of vertical data layout and intersecting,and prunes candidate frequent item sets like Apriori.Finally,the new algorithm is applied in the examination achievement analysis system.The experimental result shows that the relations will affect the students' grades,and it can be applied in guiding the students' studies and teachers' teaching practice.
作者 李翔 张伟
出处 《淮阴工学院学报》 CAS 2011年第1期40-43,共4页 Journal of Huaiyin Institute of Technology
基金 淮安市科技支撑计划项目(HAG2010069)
关键词 APRIORI算法 成绩分析 关联规则 Apriori algorithm result analysis association rule
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  • 1张文修,魏玲,祁建军.概念格的属性约简理论与方法[J].中国科学(E辑),2005,35(6):628-639. 被引量:195
  • 2徐泉清,朱玉文,刘万春.基于概念格的关联规则算法[J].计算机应用,2005,25(8):1856-1857. 被引量:11
  • 3甘特尔.形式概念分析[M].马垣,译.北京:科学出版社,2004.
  • 4Schuster A,Wolff R,Trock D.A high performance distributed algorithm for mining association rules[C].Proceedings of the 3rd IEEE International Conference on Data Mining. Florida: IEEE Computer Society,2003:291-298.
  • 5Chan M K, Ada F, Man H W. Mining Fuzzy Association Rules in Database. In:Proc. of the ACM Sixth International Conf. on Information and Knowledge Management, Las Vegas, Neveda, 1997:10-14
  • 6Hathaway R J, Davenport J W, Bezdek J C. Relational Dual of the Cmeans Algorithms. Pattern Recognition, 1989,22(2): 205-212
  • 7R. Agrawal, T. Imielinski, A. Swami. Mining association rules between sets of items in large databases. ACM SIGMOD Int'l Conf. Management of Data, Washington, D. C., 1993.
  • 8Han J, Kamber. MData Mining: Concepts and Techniques.Beijing: High Education Press, 2001.
  • 9B. Goethals. Survey of frequent pattern mining. Helsinki Institute for Information Technology, Technical Report, 2003.
  • 10R. Agrawal, R. Srikant. Fast algorithm for mining association rules. The 20th Int'l Conf. VLDB, Santiago, Chile, 1994.

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