Cart (Loading....) | Create Account
Close category search window

An approach in web content mining for clustering web pages

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Etemadi, R. ; Dept. of Electr. & Comput. Eng., Islamic Azad Univ. Branch of Bonab, Tabriz, Iran ; Moghaddam, N.

Nowadays, using web and Internet as a world wide information system faces us with so many data. In this direction, the necessity of accessing some tools for data processing in web level which helps the man intelligently to transform these data into useful knowledge seems so important. Clustering the web pages is one of these techniques. In this paper, a new algorithm has been represented to cluster web pages based on data content. The new algorithm has been suggested based on the expressions and key words existed in web pages, and their bit display a vector and using a new similarity criterion obtained from Cosine and Jaccard similarity criterion. To evaluate the efficacy of suggested algorithm, some pages with five subjects of software engineering, computerized networks, architecture of computer, parallel processing and operating system have been investigated and after preparing a suitable data bed the represented algorithm has been simulated separately through two similarity criteria of Cosine and that of represented in this pager and has been evaluated using Dunn index. The results obtained from simulation show high efficiency of the algorithm proposed in separating web pages and their clustering. The represented algorithm can be used in most of the problems related to clustering web pages.

Published in:

Digital Information Management (ICDIM), 2010 Fifth International Conference on

Date of Conference:

5-8 July 2010

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.