Synthesizing correlated RSS news articles based on a fuzzy equivalence relation

Purpose - Tens of thousands of news articles are posted online 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. This paper aims to address these issues. Design/methodology/approach - The paper presents a novel approach which uses the word-correlation factors in a fuzzy set information retrieval model to: filter out redundant news articles from RSS feeds; shed less-informative articles from the non-redundant ones; and cluster the remaining informative articles according to the fuzzy equivalence classes on the news articles. Findings - The clustering approach requires little overhead or computational costs, and experimental results have shown that it outperforms other existing, well-known clustering approaches. Research limitations/implications - The clustering approach as proposed in this paper applies only to RSS news articles; however, it can be extended to other application domains. Originality/value - The developed clustering tool is highly efficient and effective in filtering and classifying RSS news articles and does not employ any labor-intensive user-feedback strategy. Therefore, it can be implemented in real-world RSS feeds to aid users in locating RSS news articles of interest.