利用数据挖掘的网络智能感知与自适应优化

摘要

In this paper, we propose a self-optimization method based on data mining. With this method, mass data can be processed and laws can be found for self-optimization. We analyze call service prediction and show that using data mining to predict services is more accurate than using statistical methods to predict services. We present data mining technology that can be used for network prediction in a C-RAN architecture.%文章提出了一种利用数据挖掘的自适应优化方法.该方法将自优化问题与数据挖掘技术相结合,利用数据挖掘技术对海量数据进行处理,找出表示其中内在规律的知识,利用这些知识进行网络自适应优化.文章以通话业务时长预测分析为例进行分析,结果表明利用数据挖掘进行业务预测的精度高于传统的统计分析方法,论文分析在C-RAN架构下利用数据挖掘对网络状态进行预测是可行的.