By Topic

Annotation-aware web clustering based on topic model and random walks

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

4 Author(s)
Jiashen Sun ; Dept. of Comput. Sci., Beijing Univ. of Posts & Telecommun., Beijing, China ; Xiaojie Wang ; Caixia Yuan ; Guannan Fang

Web page clustering based on semantic or topic promises improved search and browsing on the web. Intuitively, tags from social bookmarking websites such as del.icio.us can be used as a complementary source to document thus improving clustering of web pages. In this paper, we present a novel model which employs topic model to associate annotated document with a distribution of topics, and then constructs a graph including tags, document and topics by performing a Random Walks for clustering. We examine the performance of our model on a real-world data set, illustrating that our model provides improved clustering performance than algorithm utilizing page text alone.

Published in:

Cloud Computing and Intelligence Systems (CCIS), 2011 IEEE International Conference on

Date of Conference:

15-17 Sept. 2011