基于关联分析FP-Tree算法的企业风险信息数据在线挖掘方法

Online mining method of enterprise risk information data based on association analysis FP-Tree algorithm

现阶段的数据挖掘方法缺少对数据关联分析的过程,挖掘效果较差,故文章提出基于关联分析频繁模式树(FrequentPattern Tree, FP-Tree)算法的企业风险信息数据在线挖掘方法。选取与企业风险相关的信息指标,收集有关数据并进行预处理操作后,设计一种考虑关联分析的FP-Tree算法,生成FP-Tree节点的条件模式树挖掘频繁项集,计算满足最小置信度的频繁项集,实现企业风险信息数据在线挖掘。实验结果表明,所用方法挖掘量和挖掘效率较高。

The current data mining methods lack the process of data association analysis,and the mining effect is poor,so the online mining method of enterprise risk information data based on the FrequentPattern Tree(FP-Tree)algorithm is proposed.After selecting information indicators related to enterprise risk,collecting relevant data and conducting pre-processing operations,an FP-Tree algorithm considering association analysis is designed to generate the conditional pattern tree of FP-Tree nodes to mine frequent item sets,calculate frequent item sets meeting the minimum confidence,and realize online mining of enterprise risk information data.The experimental results show that the method has higher excavation capacity and efficiency.