Synthesizing Correlated RSS News Articles Based on a Fuzzy Equivalence Relation

View Description

Download

small (250x250 max)

medium (500x500 max)

large ( > 500x500)

Full Resolution

PDF
Text

Description

Rating
Permanent URL	http://hdl.lib.byu.edu/1877/2638
Title	Synthesizing Correlated RSS News Articles Based on a Fuzzy Equivalence Relation
BYU Creator	Ng, Yiu-Kai D. Pera, Maria Soledad
Keywords	information retrieval; clustering; word similarity; fuzzy equivalence class; RSS news articles
Description/Abstract	Tens of thousands of news articles are posted on-line each day, covering topics from politics to science to current events. To better cope with this overwhelming volume of information, RSS (news) feeds are used to categorize newly posted articles. Nonetheless, most RSS users must filter through many articles within the same or different RSS feeds to locate articles pertaining to their particular interests. Due to the large number of news articles in individual RSS feeds, there is a need for further organizing articles to aid users in locating non-redundant, informative, and related articles of interest quickly. In this paper, we present a novel approach which uses the word-correlation factors in a fuzzy set information retrieval model to (i) filter out redundant news articles from RSS feeds, (ii) shed less-informative articles from the non-redundant ones, and (iii) cluster the remaining informative articles according to the fuzzy equivalence classes on the news articles. Our clustering approach requires little overhead or computational costs, and experimental results have shown that it outperforms other existing, well-known clustering approaches. The clustering approach as proposed in this paper applies only to RSS news articles; however, it can be extended to other application domains.
Source	Maria Soledad Pera and Yiu-Kai Ng. "Synthesizing Correlated RSS News Articles Based on a Fuzzy Equivalence Relation." International Journal of Web Information Systems (IJWIS), Volume 5, Number 1, pp. 77-109, 2009, Emerald Group Publishing Ltd.;
Publisher	Emerald Group Publishing Ltd.
Date Original	2009
Date Digital	2010-10-28
Language	English eng en
Publication Type	Peer-Reviewed Articles
Type	Text
Format	application/pdf
Owning Institution	Brigham Young University
BYU College	College of Physical and Mathematical Sciences
BYU Department	Computer Science
Copyright Status	(c) 2009 Emerald Group Publishing Ltd. This is an extended version of the ICCSA 2008 paper, Generating Fuzzy Equivalence Classes on RSS News Articles for Retrieving Correlated Information, which was selected and invited to be published by IJWIS. The original publication may be found at http://www.emeraldinsight.com/products/journals/journals.htm?id=ijwis.;
Copyright Use Information	http://www.lib.byu.edu/generic_copyright.html

Comments

Post a Comment for Synthesizing Correlated RSS News Articles Based on a Fuzzy Equivalence Relation

you wish to report:

Your comment:

Your Name:

Submit

Cancel

...

Find results with:

Searching collections:

Home Institutional Repository Synthesizing Correlated RSS News Articles Based on a Fuzzy Equivalence Relation

Synthesizing Correlated RSS News Articles Based on a Fuzzy Equivalence Relation

Description

Tags

Comments