Abnormal data mining technology in remote virtual education communication
YANG Qiong
KUANG Shan-yun
Abstract：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.
Keywords：remote virtual educationcommunicationabnormal dataminingdata characteristicclusteringdistinguishprecision
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 412-416 )