基于人工智能裁决的云网络信息数据挖掘算法

Cloud network information data mining algorithm based on artificial intelligence adjudication

针对当前云网络信息数据挖掘过程存在挖掘存储容量不足,数据挖掘效率低,挖掘成功率较差等不足,提出了一种基于人工智能裁决机制的云网络信息数据挖掘算法。引入人工智能裁决框架机制,统筹兼顾传输带宽、挖掘带宽、节点缓存等影响因素,获取数据挖掘强度指数,且通过该指数强化数据挖掘效率,实现了云网络条件下数据的高效并发挖掘。仿真实验表明:与当前广泛使用的超线性数据挖掘算法(Superlinear Data Mining algorithm,SDM算法)、螺旋自适应数据挖掘算法(Spiral Adaptive Data Mining algorithm,SADM算法)相比,文中算法能够显著提高挖掘效率,降低挖掘时延及挖掘错误率,有效改善网络因挖掘因素而导致的时延难题,具有显著的实际部署价值。

In order to solve the problem existed in the process of mining the lack of storage,data mining efficiency is low,the success rate of mining is weak,this paper presents an algorithm for mining cloud network information data frame based on gradient transcription mechanism,introduces the gradient transcription frame mechanism,integrated transmission bandwidth,bandwidth,node cache and other factors of mining influence of data mining intensity index,and the index of enhancing the efficiency of data mining,to achieve an efficient concurrent cloud network under the condition of data mining. The simulation results show that the super linear data is widely used with the current mining algorithm,spiral adaptive data mining algorithm compared this algorithm can significantly improve the efficiency of mining,mining and mining to reduce the delay error rate,greatly improve the network delay caused problems due to mining factors,has significant practical value deployment.