TY - GEN
T1 - Generating fuzzy equivalence classes on RSS news articles for retrieving correlated information
AU - Gustafson, Nathaniel
AU - Pera, Maria Soledad
AU - Ng, Yiu Kai
PY - 2008
Y1 - 2008
N2 - Tens of thousands of news articles are posted on-line each day, covering topics from politics to science to current events. In order 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 in order 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 generated 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.
AB - Tens of thousands of news articles are posted on-line each day, covering topics from politics to science to current events. In order 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 in order 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 generated 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.
UR - http://www.scopus.com/inward/record.url?scp=54249123788&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-69848-7_20
DO - 10.1007/978-3-540-69848-7_20
M3 - Conference contribution
AN - SCOPUS:54249123788
SN - 354069840X
SN - 9783540698401
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 232
EP - 247
BT - Computational Science and Its Applications - ICCSA 2008 - International Conference, Proceedings
T2 - International Conference on Computational Science and Its Applications, ICCSA 2008
Y2 - 30 June 2008 through 3 July 2008
ER -