基于概率潜在语义分析的Web用户聚类  

Web user clustering based on Probabilistic Latent Semantic Analysis

作　　者：俞辉[1] 景海峰[1] 

机构地区：[1]中国石油大学计算机与通信工程学院,山东东营257061

出　　处：《计算机工程与应用》2008年第23期160-162,共3页Computer Engineering and Applications

摘　　要：Web用户聚类知识可以为改进信息搜索效率和提供个性化服务提供帮助。通过对海量日志记录分析,构建会话-页面矩阵;根据信息论理论,在会话-页面矩阵中权值计算中考虑局部和全局权值贡献;利用概率潜在语义分析将隐式变量Z对页面P的条件概率转换为隐式变量Z对会话S的条件概率,然后在聚类分析中以此作为相似度计算依据。聚类算法采用了基于距离的k-medoids算法,以进一步改善聚类精度。实验结果验证了该算法的有效性和局限性。Knowledge of Web user clustering can improve the efficiency of information searching and personalized service.Firstly, session-page matrix can been constructed by analyzing a great deal of log.Then,based on information theory,the local weight and global weight are considered in calculation of weight in session-page matrix.With usage of probabilistic latent semantic analysis, the conditional probability of latent variable Z to page- P is transformed the conditional probability of latent variable Z to session S,then the transformed results are used in similarity calculation.The k-medoids algorithm is adopted to further improve clursting result.Experiment results validate validity and limitation of this algorithm.

关 键 词：WEB日志 预处理 WEB用户 概率潜在语义分析 聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]