基于关联规则的态势预测方法

态势预测是网络态势感知的重要环节,可以为网络管理员提供必要的决策支撑。为了实现对网络的大数据管理模式,针对当前预测算法无法充分利用大数据优势的局限,提出了基于关联规则的态势预测方法。该方法综合考虑了大数据的特点和态势预测的需求,给出了方法的基本思想和实现流程。实验结果表明,提出的方法与传统预测方法相比,通过寻找数据间的关联物而不是非线性匹配来达到预测的目的,大大降低了计算的时间复杂度,提高了预测效率。

A Situation Forecast Method Based on Association Rules

Situation forecast is a key link in network situation awareness, because this can provide necessary decision support for network managers. In order to realize the big data model of network management, a forecast method based on association rules is proposed aimed at the problem that the current forecast methods fail to take advantages of big data. The method takes the characteristics of big data into full account combined with requirement of situation forecast, and the basic idea and processes of the method are given. The experiment results show that the proposed method reduces time complexity greatly through finding relevance instead of nonlinear registration, and improves accuracy of forecast compared with the traditional methods.