01279nas a2200133   4500008004100000245005500041210005200096260002100148300001500169520080000184100002100984700002201005856011801027       2010                        eng d00aW - kmeans: Clustering News Articles using WordNet0 aW kmeans Clustering News Articles using WordNet  cSeptember 8 - 10  a 379 - 3883 aDocument clustering is a powerful technique that has been widely
used for organizing data into smaller and manageable information kernels.
Several approaches have been proposed suffering however from problems like
synonymy, ambiguity and lack of a descriptive content marking of the
generated clusters. We are proposing the enhancement of standard kmeans
algorithm using the external knowledge from WordNet hypernyms in a twofold
manner: enriching the “bag of words” used prior to the clustering process and
assisting the label generation procedure following it. Our experimentation
revealed a significant improvement over standard kmeans for a corpus of news
articles derived from major news portals. Moreover, the cluster labeling process
generates useful and of high quality cluster tags.1 aBouras, Christos1 aTsogkas, Vassilis  uhttps://telematics.upatras.gr/telematics/publications/w-kmeans-clustering-news-articles-using-wordnet?language=el