01208nas a2200133   4500008004100000245003500041210003500076260001500111300000800126520081100134100002100945700002200966856008600988       2010                        eng d00aAssigning Web News to Clusters0 aAssigning Web News to Clusters  cMay 9 - 15  a1-53 aThe Web is overcrowded with news articles, an
overwhelming information source both with its amount and
diversity. Assigning news articles to similar groups, on the 
other hand, provides a very powerful data mining and
manipulation technique for topic discovery from text
documents. In this paper, we are investigating the application 
of a great spectrum of clustering algorithms, as well as 
similarity measures, to news articles that originate from the 
Web and compare their efficiency for use in an online Web
news service application. We also examine the effect of
preprocessing on clustering. Our experimentation showed that
k-means, despite its simplicity, accompanied with preliminary
steps for data cleaning and normalizing, gives better aggregate
results when it comes to efficiency.1 aBouras, Christos1 aTsogkas, Vassilis  uhttps://telematics.upatras.gr/telematics/publications/assigning-web-news-clusters