内容中心网络移动终端数据优化挖掘模型仿真

Content Centric Networking Mobile Terminal Data Optimization Mining Mode Simulationl

内容中心网络中,Internet用户通常更加关心移动终端的数据内容,需要对此进行有效挖掘,传统的内容中心网络移动终端数据挖掘模型采用关联性辅助挖掘算法,由于内容中心网络的数据之间的关联性是自反的和传递的,导致挖掘效果不好。提出一种基于压缩频谱联合特征识别的内容中心网络移动终端数据优化挖掘模型。构建网络模型和数据挖掘结构模型,进行移动终端数据提取预处理,该内容块被划分成多个分片,把特征数据并行化地存储到不同的存储服务器中,实现对移动终端数据的压缩频谱联合特征识别,达到数据挖掘的目的。仿真实验表明,采用该模型进行数据挖掘,具有较大数据挖掘吞吐量,使得数据存储对象数目有明显提升,数据挖掘精度和收敛性能得到提高。

In content centric networking, Internet users tend to care more about the content of mobile data terminal, the need to effectively mine, traditional content center network mobile terminal data mining model uses association assisted mining algorithm, because the content center network number according to relevance between is reflexive and transitive, lead mining effect good. Proposed a compressed spectrum combined with feature recognition content center network based on mobile terminal optimized data mining model. The construction of network model and data mining structure model, ex-traction pretreatment mobile terminal data, the content block is divided into a plurality of pieces, the parallel storage to stor-age servers in different characteristics in the data, realize the compression joint feature spectrum identification of mobile terminal data, to achieve the purpose of data mining. Simulation results show that using the model of data mining, data min-ing has a large number of data throughput, which makes the storage object has improved significantly, the data mining accu-racy and convergence performance is improved.