[1] Lu Jianjiang, Xu Baowen, Huang Gangshi, et al. Matrix dimensionality reduction for mining typical user profiles [J]. Journal of Southeast University (English Edition), 2003, 19 (3): 231-235. [doi:10.3969/j.issn.1003-7985.2003.03.006]

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Matrix dimensionality reduction for mining typical user profiles()

基于矩阵降维的典型用户文件发现方法

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

19

Issue:

2003 3

Page:

231-235

Research Field:

Automation

Publishing date:

2003-09-30

Info

Title:

Matrix dimensionality reduction for mining typical user profiles

基于矩阵降维的典型用户文件发现方法

Author(s):

Lu Jianjiang^{1, 2}, Xu Baowen^{1, 3}, Huang Gangshi², Zhang Yafei²

¹Department of Computer Science and Engineering, Southeast University, Nanjing 210096, China
²School of Science, PLA University of Science and Technology, Nanjing 210007, China
³School of Computer Science, National

陆建江^{1, 2}, 徐宝文^{1, 3}, 黄刚石², 张亚非²

¹东南大学计算机科学与工程系, 南京 210096; ²解放军理工大学理学院, 南京 210007; ³国防科学技术大学计算机学院, 长沙 410073

Keywords:

Web usage mining;  non-negative matrix factorization;  spherical k-means algorithm

Web挖掘;  非负矩阵分解;  球形的k-均值算法

PACS:

TP18

DOI:

10.3969/j.issn.1003-7985.2003.03.006

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.

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.

References:

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Memo

Memo:

Biographies: Lu Jianjiang(1968—), male, associate professor; Xu Baowen(corresponding author), male, doctor, professor, bwxu@seu.edu.cn.

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