By Topic

Assigning Web News to Clusters

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)
Bouras, C. ; Comput. Eng. & Inf. Dept., Univ. of Patras, Patras, Greece ; Tsogkas, V.

The Web is overcrowded with news articles, an overwhelming information source both with its amount and diversity. Assigning news articles to similar groups, on the other hand, provides a very powerful data mining and manipulation technique for topic discovery from text documents. In this paper, we are investigating the application of a great spectrum of clustering algorithms, as well as similarity measures, to news articles that originate from the Web and compare their efficiency for use in an online Web news service application. We also examine the effect of preprocessing on clustering. Our experimentation showed that k-means, despite its simplicity, accompanied with preliminary steps for data cleaning and normalizing, gives better aggregate results when it comes to efficiency.

Published in:

Internet and Web Applications and Services (ICIW), 2010 Fifth International Conference on

Date of Conference:

9-15 May 2010