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.
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