Friday 29th of March 2024
 

Clustering Web Access Patterns Based on Learning Automata


Babak Anari, Mohammad Reza Meybodi and Zohreh Anari

The interest of web users can be revealed by the visited web pages and time duration on these web pages during their surfing. In this paper a new method based on Learning Automata for clustering web access patterns is proposed. At the first step of the proposed algorithm, each web access pattern from web logs is transformed into a weight vector using the learning automata. In the second step a primitive clustering is performed to group weight vectors into a number of clusters. Finally, the weighted Fuzzy c-means approach is developed to deal with the results of the second step. Our experiments on a large real data set show that the method is efficient and practical for web mining applications.

Keywords: Web access patterns, Clustering, Learning automata, Distributed learning automata, Time duration

Download Full-Text


ABOUT THE AUTHORS

Babak Anari
Member Of Faculty of azad university of shabestar

Mohammad Reza Meybodi
Member Of Faculty of Amirkabir University of Technology, Tehran

Zohreh Anari
Member Of Faculty of azad university of shabestar


IJCSI Published Papers Indexed By:

 

 

 

 
+++
About IJCSI

IJCSI is a refereed open access international journal for scientific papers dealing in all areas of computer science research...

Learn more »
Join Us
FAQs

Read the most frequently asked questions about IJCSI.

Frequently Asked Questions (FAQs) »
Get in touch

Phone: +230 911 5482
Email: info@ijcsi.org

More contact details »