摘要
高校学生的招生、就业等信息数量庞大,表目繁多,对这些数据有效地进行预处理,并进一步挖掘以获得有利于高校教学管理决策和毕业生就业指导的有用信息,具有重要意义.以沈阳市某高校学生招生就业数据为基础,建立了一个基于学生信息的关联规则挖掘系统,并对其中的Apriori算法进行优化,同时对由频繁项集生成关联规则的算法给予改进,挖掘结果中产生了大量有益信息,通过实际检验,该优化算法能避免大量无意义关联规则的产生并提高挖掘效率.
Due to the vast amount of data in both enrollment and employment of undergraduate students, it is necessary to preprocess and mine the data in order to obtain useful information for the decision making in teaching management and the employment instruction of undergraduate students. An association rules mining system of student information was established based on the association rules and based on the enrollment and employment data of students from a university in Shenyang. The Apriori algorithm in the system was optimized and the algorithm to generate association rules from the frequent items was improved. The mining results produced a lot of useful information. Practical tests have proved that the algorithm can prevent generating lots of meaningless rules and improve mining efficiency.
出处
《沈阳工业大学学报》
EI
CAS
2007年第3期344-346,共3页
Journal of Shenyang University of Technology
作者简介
闫禹（1969-），男，辽宁沈阳人，讲师，博士生，主要从事数据库及数据挖掘方面的研究．
