远程虚拟教育通信中异常数据挖掘技术

Abnormal data mining technology in remote virtual education communication

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.

Aiming at the low accuracy and poor efficiency problems when the traditional mining methods are applied to the abnormal data mining in the remote virtual education communication, an abnormal data mining method based on FWSCA and differential evolution method in the remote virtual education was proposed. The data characteristics of remote virtual education communication were extracted with the information gain method. In addition, the data characteristics of online communication were clustered with the introduction of WTA rule. On this basis, the data were distinguished with the sparse score method, and the FWSCA in combination with the differential evolution method was adopted to conduct the abnormal data mining in the remote virtual education communication. The results show that when it is used for data mining, the proposed method exhibits higher mining precision and short mining time, and has certain advantages compared with the traditional mining algorithm.