基于领域关联冗余的教务数据关联规则挖掘  

Educational Administration Data Mining of Association Rules Based on Domain Association Redundancy

作　　者：陆鑫赟 王兴芬[2] LU Xin-yun;WANG Xing-fen(Computer School,Beijing Information Science and Technology University,Beijing100192,China;School of Information management,Beijing Information Science and TechnologyUniversity,Beijing 100192,China)

机构地区：[1]北京信息科技大学计算机学院,北京100192 [2]北京信息科技大学信息管理学院,北京100192

出　　处：《计算机科学》2019年第B06期427-430,435,共5页Computer Science

摘　　要：教育教学的周期性以及教学环境的变化使高校教务数据具有时序性的特点,并且高校教务数据存在较多的关联冗余,因此挖掘出高效有趣的关联规则较为困难。虽然序列模式挖掘算法能够挖掘出时序频繁项集,但其并不能消除教务数据中的关联冗余,挖掘结果的效用性以及新颖性均无法满足要求。为此,文中提出了一种基于教育领域关联冗余的FUI_DK关联规则挖掘算法。FUI_DK算法基于序列模式挖掘算法产生频繁候选项集,在经典关联规则算法的支持度、置信度的基础上增加效用度以及有趣度这两个参数来得到高效用有趣项集,并根据满足条件的关联规则的支持度、置信度、效用度对其进行排序输出,最终得到具有高效用性以及有趣性的关联规则结果。在某高校学生教务数据上进行实验对比及挖掘结果分析,实验证明该算法缩短了运算时间,领域内已知关联规则的消除率可达43%,可帮助高校进行省时有效的教育数据挖掘。Due to the periodicity of teaching and the change of teaching environment,the data of educational administration in colleges and universities have the characteristics of time series,and there are many association redundancy,so it is difficult to find out the efficient and interesting association rules.Although the sequential pattern mining algorithm can mine the time series frequent itemsets,it can not eliminate the association redundancy in educational administration data,and the utility and novelty of mining results can not meet the requirements.Therefore,this paper proposed a FUI_DK association rule mining algorithm based on association redundancy in the educational field.FUI_DK algorithm generates frequent candidate itemsets based on sequential pattern mining algorithm,and increases utility and interest to obtain high utility interesting itemsets based on the support,confidence of classical association rule algorithms,and the association rules satisfying the conditions are sorted out according to their support,confidence and utility.Finally,the result of association rules with high utility and high interest is obtained.The experiment contrast and mining result analysis are carried out on the data of a university student educational administration.The experimental results show that the FUI_DK algorithm has better time performance in the data mining of university educational administration,and the elimination rate of known association rules in the field can reach 43%,which can help colleges and universities to carry out time-saving and effective educational data mining.

关 键 词：序列模式挖掘 关联规则 高效用有趣项集 领域知识 教务数据 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]