01302nas a2200133   4500008004100000245006000041210005800101260001700159300001000176520082800186100002101014700002201035856011101057       2013                        eng d00aEnhancing news articles clustering using word n - grams0 aEnhancing news articles clustering using word n grams  cJuly 29 - 31  a53-603 a<p>In this work we explore the possible enhancement of the document clustering results, and in particular clus-tering of news articles from the web, when using word-based n-grams during the keyword extraction phase. We present and evaluate a weighting approach that combines clustering of news articles derived from the web using n-grams, extracted from the articles at an offline stage. We compared this technique with the sin-gle minded bag-of-words representation that our clustering algorithm, W-kmeans, previously used. Our ex-perimentation revealed that via tuning of the weighting parameters between keyword and n-grams, as well as the n itself, a significant improvement regarding the clustering results metrics can be achieved. This re-flects more coherent clusters and better overall clustering performance.</p>
1 aBouras, Christos1 aTsogkas, Vassilis  uhttp://telematics.upatras.gr/telematics/publications/enhancing-news-articles-clustering-using-word-n-grams