【摘要】 应用聚类技术能够自动地发现典型用户文件 ,但是由于会话向量通常是高维的稀疏向量 ,因此很难在会话向量之间设计有效的相似度度量 .本文提出 2种基于矩阵降维的典型用户文件发现方法 .这些方法应用非负矩阵分解技术降低会话 URL矩阵的维数 ,并通过球形的k 均值算法对用户会话向量的投影向量聚类 ,由此得到典型用户文件 .实验结果表明 ,这些算法能够有效地从用户会话中发现典型的用户文件 .更多还原

【Abstract】 Recently clustering techniques have been used to automatically discover typical user profiles. In general, it is a challenging problem to design effective similarity measure between the session vectors which are usually high-dimensional and sparse. Two approaches for mining typical user profiles, based on matrix dimensionality reduction, are presented. In these approaches, non-negative matrix factorization is applied to reduce dimensionality of the session-URL matrix, and the projecting vectors of the user-session vectors are clustered into typical user-session profiles using the spherical k -means algorithm. The results show that two algorithms are successful in mining many typical user profiles in the user sessions.更多还原