Web使用挖掘在网络学习中的应用研究

Applied Research on Web Usage Mining in Web Learning

在讨论Web使用挖掘在网络学习中的应用过程中,提出一种改进的基于向量的聚类算法.在算法中,首先以学习站点的URL为行、以UserID为列建立页面用户关联矩阵,元素值为学习者的访问次数,然后使用欧氏距离进行度量向量之间的相似性,对列向量进行相似性分析得到相似学习者群体,对行向量进行相似性度量获得相关Web页面.分析表明,Web使用挖掘在网络学习中的应用是可行、有效的.

In discussing the application of usage mining in web learning, an improved vector＇s based cluster- ing algorithm is presented. First, according to the web site＇s structure a URL-UserID relevant matrix is set up, in which URL is used as row and UserID as column, and each element＇s value of the matrix is the learner＇s hits. Sec- ond, similar vectors are measured by computing the euclidean distane between the two vectors. Relevant web pages are found by measuring the similarity between row vecotrs. Similar learner groups are discovered by measuring the similarity between column vectors. Finally, the results show that the application of web usage mining in web learn- ing is feasible and effective.