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...

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Bibliographic Details
Published in:International journal of Web information systems 2009-01, Vol.5 (1), p.77-109
Main Authors: Soledad Pera, Maria, Ng, YiuKai
Format: Article
Language:English
Subjects:
classification schemes
Fuzzy sets
Information media
Information retrieval
Internet
Mathematical models
News
Online information retrieval
RSS
Studies
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Summary: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 nonredundant, informative, and related articles of interest quickly. This paper aims to address these issues. Designmethodologyapproach The paper presents a novel approach which uses the wordcorrelation factors in a fuzzy set information retrieval model to filter out redundant news articles from RSS feeds shed lessinformative articles from the nonredundant 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, wellknown clustering approaches. Research limitationsimplications The clustering approach as proposed in this paper applies only to RSS news articles however, it can be extended to other application domains. Originalityvalue The developed clustering tool is highly efficient and effective in filtering and classifying RSS news articles and does not employ any laborintensive userfeedback strategy. Therefore, it can be implemented in realworld RSS feeds to aid users in locating RSS news articles of interest.
ISSN:1744-0084
1744-0092
DOI:10.1108/17440080910947321