远程虚拟教育通信中异常数据挖掘技术

发布时间：2018-03-10 04:31

本文选题：远程虚拟教育　切入点：通信　出处：《沈阳工业大学学报》2017年04期 　论文类型：期刊论文

【摘要】：针对现有挖掘方法应用到远程虚拟教育通信异常数据挖掘时,其挖掘准确率及应用效率偏低的问题,提出一种基于空间聚类算法(FWSCA)与差分进化法的远程虚拟教育中异常数据挖掘方法.采用信息增益法提取远程虚拟教育通信数据特征,引入WTA规则对在线通信的数据特征进行聚类,在此基础上,采用稀疏分数方法对数据进行区分,采用FWSCA与差分进化法相结合对运程虚拟教育通信异常数据进行挖掘.结果表明,采用该挖掘方法进行异常数据挖掘,挖掘精度相比传统挖掘算法精度高、时间短,具有一定的优势.
[Abstract]:Aiming at the problem of low mining accuracy and application efficiency when the existing mining methods are applied to the remote virtual education communication abnormal data mining, the mining accuracy and the application efficiency of the mining methods are on the low side. An anomaly data mining method in distance virtual education based on spatial clustering algorithm (FWSCA) and differential evolution method is proposed. The information gain method is used to extract the data features of remote virtual education communication. The WTA rule is introduced to cluster the data features of online communication. On this basis, the sparse fraction method is used to distinguish the data, and the FWSCA and differential evolution method are combined to mine the abnormal data of the virtual educational communication. Using this mining method to mine abnormal data, the precision of mining is higher than that of the traditional mining algorithm, and the time is short, so it has some advantages.
【作者单位】：华东师范大学教育科学学院;琼台师范学院信息技术系;华南师范大学教育信息技术学院;
【基金】：海南省高校教育教学改革研究项目(Hnjg2015-81)
【分类号】：G434;TP311.13

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