基于矩阵降维的典型用户文件发现方法

应用聚类技术能够自动地发现典型用户件，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．本提出2种基于矩阵降维的典型用户件发现方法．这些方法应用非负矩阵分解技术降低会话-URL矩阵的维数，并通过球形的后．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件．

Matrix dimensionality reduction for mining typical user profiles

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.