一种基于关系熵和J量值的网络事件关联模式漂移检测方法  

Pattern Drift Detection in Association Analysis Based on Relation Entropy and J Measure

作　　者：杨英杰[1] 刘帅[1] 常德显[1] 

机构地区：[1]解放军信息工程大学,郑州450001

出　　处：《计算机科学》2016年第1期259-263,共5页Computer Science

基　　金：国家"863"计划资助项目(2012AA012704);郑州市科技领军人才项目(131PLJRC644)资助

摘　　要：针对传统的模式漂移检测方法无法直接适用于关联规则分析的问题,提出了一种基于关系熵和J量值的模式漂移检测方法。抽取并定义了4种特征属性:关系量RC、关系熵RE、窗口数WC和J量值,并通过对时间相邻的两个滑动窗口内的关系熵和J量值的取值分布进行假设检验,判断和定位模式漂移的发生,得到模式漂移影响较大的事件集和事件对集,它们可为关联规则的调整和更新提供支持。实验数据表明该方法准确可行。Aiming at the problem that traditional concept drift detection technology cannot be applied in association analysis directly,this paper proposed a method of concept drift detection based on relation entropy and J measure.Four features such as relation count,relation entropy,windows count and J measure are extracted.Concept drift is detected and located through hypothesis test of features distribution in two sliding windows.Event set and event pair set mostly effected by concept drift which can provide support to adjustment and refreshment of concept are obtained.Experimental results show that this method is accurate and feasible.

关 键 词：关联模式 模式漂移 关系熵 J量值 假设检验 模式漂移检测 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]