Web Site Auditing Using Web Access Log Data

Author

He, Si ; Balecel, Nabil ; Hamam, Habib ; Bouslimani, Yassine

Author_Institution

Electr. Eng. Dept., Univ. de Moncton Moncton, Moncton, NB

fYear

2009

fDate

11-13 May 2009

Firstpage

94

Lastpage

101

Abstract

This paper applies a method to use the access log data to audit Web sites. It studies website auditing by (1) proposing a new fuzzy clustering algorithm that combines standard fuzzy C-means and the artificial fish swarm algorithm; (2) presenting a new measurement index for similarities between user sessions; and (3) providing an experiment on the execution of this new method. The results are encouraging and show the potential of our fuzzy clustering approach to assist in auditing Web site.

Keywords

Web sites; artificial intelligence; auditing; information retrieval; optimisation; pattern clustering; Web access log data; Web site auditing; artificial fish swarm algorithm; fuzzy clustering algorithm; standard fuzzy C-means; Clustering algorithms; Communication networks; Companies; Councils; Data analysis; Helium; Information technology; Marine animals; Measurement standards; Web sites; Artificial Fish Swarm Algorithm; Fuzzy Clustering; Web Access Log; Web site auditing;

fLanguage

English

Publisher

ieee

Conference_Titel

Communication Networks and Services Research Conference, 2009. CNSR '09. Seventh Annual

Conference_Location

Moncton, NB

Print_ISBN

978-1-4244-4155-6

Electronic_ISBN

978-0-7695-3649-1

Type

conf

DOI

10.1109/CNSR.2009.24

Filename

4939112