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基于关联规则挖掘算法的教务管理系统设计 被引量:4

Development of the educational administration system based on association rule mining algorithm
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摘要 针对当前教务管理系统的信息化需求,结合当前的智能推荐技术,提出一种基于关联规则挖掘算法的教务管理系统。基于软件工程设计思想,首先对系统的功能需求进行分析;然后从逻辑架构、网络拓扑结构、功能设计等方面对系统进行初步设计,并结合Apriori算法原理,提出了基于兴趣度的关联规则算法模型。最后,给出了该系统的登录界面和课程推荐界面。 In view of the information requirement of the current educational administration system and the current intelligent recommendation technology,it introduces a kind of educational administration system based on association rules mining algorithm.Based on the idea of software engineering design,it analyzes the functional requirements of the system,designs the system such as logical architecture,network topology and function model,proposes the model of association rules algorithm based on the degree of interest in combination with the principle of Apriori algorithm.Finally,it shows the login interface and recommended interface of the system.
作者 叶梦雄 Ye Mengxiong(Xi'an Aerotechnical Polytechnic College,Shaanxi Xi'an,710089,China)
出处 《机械设计与制造工程》 2018年第8期123-126,共4页 Machine Design and Manufacturing Engineering
关键词 APRIORI算法 教务管理 移动客户端 Apriori algorithm educational administration mobile client
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  • 1陈爱东,刘国华,费凡,周宇,万小妹,貟慧.满足均匀分布的不确定数据关联规则挖掘算法[J].计算机研究与发展,2013,50(S1):186-195. 被引量:18
  • 2宋余庆,朱玉全,孙志挥,杨鹤标.一种基于频繁模式树的约束最大频繁项目集挖掘及其更新算法[J].计算机研究与发展,2005,42(5):777-783. 被引量:21
  • 3马建庆,钟亦平,张世永.基于兴趣度的关联规则挖掘算法[J].计算机工程,2006,32(17):121-122. 被引量:20
  • 4刘学军,徐宏炳,董逸生,钱江波,王永利.基于滑动窗口的数据流闭合频繁模式的挖掘[J].计算机研究与发展,2006,43(10):1738-1743. 被引量:26
  • 5AGRAWAL R, IMIELINSKI T, SWAMI A. Mining association rules between sets of items in large databases [ J ]. AGM SIGMOD Re- cord,1993,22(2) :207-216.
  • 6KAMSU-FOGUEM B, RIGAL F, MAUGET F. Mining association rules for the quality improvement of the production process [ J ]. Ex- pert Systems with Applications,2013,40 (4) :1034-1045.
  • 7QODMANAN H R, NASIRI M, MINAEI-BIDGOLI B. Multi objec- tive association rule mining with genetic algorithm without specifying minimum support and minimum cmffidence [ J ]. Expert Systems with Applications ,2011,38( 1 ) :288-298.
  • 8ZAKI M J. Mining non-redundant association rules[ J]. Data Mining and Knowledge Discovery, 2004,9 ( 3 ) : 223 - 248.
  • 9GENG Li-qiang, HAMILTON H J. Interestingness measures for data mining: a survey [ J ]. AOM Computing Surveys, 2006,88 ( 3 ) : 1 - 32.
  • 10LI Jiu-yong. On optimal rule discovery[ J]. IEEE Trans on Know- ledge and Data Engineering,2006,18(4) :460-471.

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