Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CNSR.2009.24 | CNSR |
Keywords | Field | DocType |
optimisation,web access log data,pattern clustering,web access log,artificial intelligence,fuzzy clustering approach,user session,access log data,artificial fish swarm algorithm,information retrieval,standard fuzzy c-means,new measurement index,fuzzy clustering,web site auditing,web sites,fuzzy clustering algorithm,auditing,artificial fish,new fuzzy clustering algorithm,website auditing,swarm algorithm,new method,web pages,data analysis,helium,data mining,communication networks,indexation,information technology,clustering algorithms,web accessibility | Fuzzy clustering,Data mining,Audit,Web page,Swarm behaviour,Computer science,Fuzzy logic,Digital audio broadcasting,Cluster analysis,Web site | Conference |
ISBN | Citations | PageRank |
978-0-7695-3649-1 | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Si He | 1 | 16 | 1.78 |
| Nabil Balecel | 2 | 0 | 0.34 |
| habib hamam | 3 | 124 | 23.13 |
| Yassine Bouslimani | 4 | 17 | 2.50 |