一种数据挖掘算法在课程中的研究

Research on A Data Mining Algorithm in Curriculum

针对关联规则挖掘算法中存在事务之间的某种关联性的数据无法区分的问题,本文将蚁群算法和加权概念引入到挖掘算法中,首先分析了目前加权关联规则挖掘算法的不足,提出了将蚁群算法引入到加权关联规则中,并对蚁群算法中的应度函数,状态转移规则和信息素更新的改进,并采用矩形向量作为事务存储结构进行连接和剪枝。仿真实验中与基本Apriori算法进行比较,并通过将本校的课程资源作为挖掘对象,结果说明本文算法具有良好的挖掘效果。

Because the certain associated data in association rule mining algorithm cannot be distinguished in matters, the ant colony algorithm and the weighted concept are introduced in this paper. First of all, deficiencies of the current weighted association rule mining algorithm are analyzed, and it is proposed to introduce ant colony algorithm into weighted association rules and then update and improve the fitness function, status transfer rules and update of pheromone, and the rectangle vector is used as transaction storage structure for connection and pruning. The simulation experiments are compared with the basic Apriori algorithm, and the course resources are taken as the mining objects. The results show that the algorithm has a good mining effect.