基于关联规则的Web日志异常数据挖掘模型  

Web Log Exception Data Mining Model Based on Association Rules

作　　者：赵艳 ZHAO Yan(Tianjin 78th Middle School,Tianjin 300000,China)

机构地区：[1]天津市第七十八中学,天津300000

出　　处：《信息与电脑》2023年第11期50-52,共3页Information & Computer

摘　　要：常规Web日志异常数据挖掘模型使用Rough Sets粗集理论挖掘异常日志隐含信息,易受异常数据集的近似分类作用影响,导致挖掘相似度偏低,为此设计基于关联规则的Web日志异常数据挖掘模型。实验结果表明,与对比模型相比,该模型的挖掘相似度较高,性能良好,能够优化Web日志推荐效果。The conventional Web log anomaly data mining model uses Rough Sets rough set theory to mine the hidden information of anomaly logs,which is easily affected by the approximate classification effect of the anomaly dataset,resulting in low mining similarity.Therefore,a Web log anomaly data mining model based on association rules is designed.The experimental results show that compared with the comparative model,this model has higher mining similarity and good performance,and can optimize the recommendation effect of Web logs.

关 键 词：关联规则 WEB 日志 异常数据 挖掘模型 

分 类 号：TN06[电子电信—物理电子学]