Web user clustering based on Probabilistic Latent Semantic Analysis

doi:10.3778/j.issn.1002-8331.2008.23.049

Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (23): 160-162.DOI: 10.3778/j.issn.1002-8331.2008.23.049

• 数据库、信号与信息处理 • Previous Articles Next Articles

Web user clustering based on Probabilistic Latent Semantic Analysis

YU Hui,JING Hai-feng

Institute of Computer & Communication Engineering，China University of Petroleum，Dongying，Shandong 257061，China

Received:2007-10-15 Revised:2008-01-28 Online:2008-08-11 Published:2008-08-11
Contact: YU Hui

基于概率潜在语义分析的Web用户聚类

俞辉,景海峰

中国石油大学计算机与通信工程学院，山东东营 257061

通讯作者: 俞辉

Abstract

Abstract: 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.

Key words: Web log, preprocessing, Web user, Probabilistic Latent Semantic Analysis（PLSA）, clustering

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。

关键词: Web日志, 预处理, Web用户, 概率潜在语义分析, 聚类

YU Hui,JING Hai-feng. Web user clustering based on Probabilistic Latent Semantic Analysis[J]. Computer Engineering and Applications, 2008, 44(23): 160-162.

俞辉,景海峰. 基于概率潜在语义分析的Web用户聚类[J]. 计算机工程与应用, 2008, 44(23): 160-162.

[1]	LAN Hong, HUANG Min. Fusion of KNN Optimized Density Peaks and FCM Clustering Algorithm [J]. Computer Engineering and Applications, 2021, 57(9): 81-88.
[2]	GUO Xiaojing, SUI Haoda. Application of Improved YOLOv3 in Foreign Object Debris Target Detection on Airfield Pavement [J]. Computer Engineering and Applications, 2021, 57(8): 249-255.
[3]	LI Li, JI Xinyuan, SONG Song. Prediction Model for Number of Software Defects in Loop [J]. Computer Engineering and Applications, 2021, 57(7): 158-163.
[4]	HUO Guangyu, ZHANG Yong, SUN Yanfeng, YIN Baocai. Research on Archive Data Intelligent Classification Based on Semantic [J]. Computer Engineering and Applications, 2021, 57(6): 247-253.
[5]	YANG Fang, YIN Xi, SI Jianhui, LIU Hongyuan, WANG Xue. Mathematical Expression Similarity Calculation Method Based on Focus Clustering [J]. Computer Engineering and Applications, 2021, 57(6): 88-93.
[6]	ZHAO Fan, ZHANG Lin, WEN Zhiquan, YANG Linlin, LIN Guangfeng. Direct and Efficient Natural Scene Chinese Character Approaching Spotting Method [J]. Computer Engineering and Applications, 2021, 57(6): 159-167.
[7]	PENG Qihui, XUAN Shibin, GAO Qing. Distribution Automatic Threshold Density Peak Clustering Algorithm [J]. Computer Engineering and Applications, 2021, 57(5): 71-78.
[8]	LI Yongzhen, LIAO Husheng. Multi-view Clustering via Graph Convolutional Neural Network [J]. Computer Engineering and Applications, 2021, 57(5): 115-122.
[9]	WANG Changlong, ZHANG Yuandong, MIAO Hong, YANG Yuheng. Application of Double Channel Convolutional Neural Network in Pumpkin Diseases Identification [J]. Computer Engineering and Applications, 2021, 57(5): 183-189.
[10]	HU Xiaomin, WANG Mingfeng, ZHANG Shourong, LI Min. New Differential Evolution with Particle Swarm Optimization Algorithm for Text Clustering [J]. Computer Engineering and Applications, 2021, 57(4): 61-67.
[11]	WANG Junling, LU Xinming. Video Key Frame Extraction Algorithm Based on Semantic Correlation [J]. Computer Engineering and Applications, 2021, 57(4): 192-198.
[12]	WANG Fuyin, ZHANG Desheng, ZHANG Xiao. Adaptive Density Peaks Clustering Algorithm Combining with Whale Optimization Algorithm [J]. Computer Engineering and Applications, 2021, 57(3): 94-102.
[13]	CHEN Junfeng, ZHENG Zhongtuan. Over-Sampling Method on Imbalanced Data Based on WKMeans and SMOTE [J]. Computer Engineering and Applications, 2021, 57(23): 106-112.
[14]	ZHANG Zhonglin, ZHAO Yu, YAN Guanghui. Natural Neighbor Density Extremum Clustering Algorithm [J]. Computer Engineering and Applications, 2021, 57(23): 200-210.
[15]	MEI Jie, WEI Yuanyuan, XU Taosheng. Fusion Clustering Algorithm Based on Multi-Prototypes Using Density Peaks [J]. Computer Engineering and Applications, 2021, 57(22): 78-85.

Web user clustering based on Probabilistic Latent Semantic Analysis

基于概率潜在语义分析的Web用户聚类

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics