information retrieval; clustering; word similarity; fuzzy equivalence class; RSS news articles
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.
(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.;