面向个性化继续教育的关联规则挖掘算法研究

Research on association rule mining algorithm for personalized continuing education

针对个性化继续教育服务的智能优化需求,文中提出了一套基于关联规则挖掘算法的个性化继续教育方案智能优化模型。在对系统总体模型进行分模块架构的基础上,实现了关联规则挖掘算法的数据预处理,并由此建立了频繁项集与强关联规则,可以使模型有效地挖掘出关联数据,从而更精确地优化教育方案。同时,通过引入DBSCAN聚类算法,在模型中对数据进行了多属性间的自适应聚类,更好地挖掘出了数据之间的强关联规则。仿真与数据分析结果表明,文中所提算法模型相对于现有技术方案,可以分析挖掘出更为合理的关联规则,能够精准地实现个性化继续教育方案的有效优化。

In view of the intelligent optimization demand of personalized continuing education service,a set of intelligent optimization model of personalized continuing education scheme based on association rule mining algorithm is proposed in this paper.Based on the module structure of the overall model of the system,the data preprocessing of the association rule mining algorithm is realized,and the frequent itemsets and strong association rules are established,which can effectively mine the association data and optimize the education scheme more accurately.At the same time,through the introduction of DBSCAN clustering algorithm,the data in the model for multi⁃attribute adaptive clustering,better mining the strong association rules between data.Simulation and data analysis results show that the proposed algorithm model can analyze and mine more reasonable association rules compared with the existing technical solutions,and can accurately realize the effective optimization of personalized continuing education scheme.