Web User Access Pattern Mining Based on Kohonen Neural Network

Author

Long-Zhen Duan ; Mei Fan ; Long-Jun Huang

Author_Institution

Coll. of Inf. & Eng., Nanchang Univ.

fYear

2006

fDate

Dec. 2006

Firstpage

63

Lastpage

68

Abstract

This paper provides a method for Web user access pattern mining based on kohonen neural network. User session vectors were first input to kohonen network, after training we found several clusters, compute the median of each cluster and characterize what the cluster represents with the URLs. When the online user requests URLs, a matching category is found according to the pages the user has accessed. Pages that the use has not accessed so far and will access are included as suggestions and links in the html to the user. This method is efficient in user access pattern mining and from it we can provide personalize services in order to succeed in the competition of Web services

Keywords

Internet; data mining; neural nets; Kohonen neural network; Web services; Web user access pattern mining; matching category; Computer networks; Data mining; Educational institutions; HTML; Intelligent agent; Neural networks; Neurons; Phased arrays; Uniform resource locators; Web server;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Intelligence and Intelligent Agent Technology Workshops, 2006. WI-IAT 2006 Workshops. 2006 IEEE/WIC/ACM International Conference on

Conference_Location

Hong Kong

Print_ISBN

0-7695-2749-3

Type

conf

DOI

10.1109/WI-IATW.2006.145

Filename

4053205