Open AccessSynthesizing correlated RSS news articles based on a fuzzy equivalence relation
Author(s) -
Maria Soledad Pera,
YiuKai Ng
Publication year - 2009
Publication title -
international journal of web information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.186
H-Index - 18
eISSN - 1744-0092
pISSN - 1744-0084
DOI - 10.1108/17440080910947321
Subject(s) - rss , computer science , cluster analysis , information retrieval , categorization , data mining , relation (database) , filter (signal processing) , news aggregator , overhead (engineering) , set (abstract data type) , world wide web , machine learning , artificial intelligence , computer vision , programming language , operating system
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 classe...
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