P.  Manimaran, K. Duraiswamy  , 
    Web Page Clustering Based on Novel Latent Semantic Approach,
    International Journal of Soft Computing,
    Volume 8,Issue 3,
    2013,
    Pages 149-153,
    ISSN 1816-9503,
    ijscomp.2013.149.153,
    (https://makhillpublications.co/view-article.php?doi=ijscomp.2013.149.153)
    Abstract: Clustering algorithms are usually based on the Bag-of-Words 
  (BOW) approach. A tarnished hindrance of the BOW prototypical is that it ignores 
  the semantic relationship among words. As a result, if two documents use different 
  collections of core words to represent the same topic, they may be assigned 
  to different clusters even though the core words they use are probably synonyms 
  or semantically associated in other form and other disadvantage of conventional 
  web page clustering technique is often utilized to reveal the functional similarity 
  of WebPages. Tagging can be beneficial to improve the clustering performance. 
  Several efforts have been made to explore social tagging for clustering. But 
  there is some drawbacks of tagging web based clustering. All the existing approaches 
  exploiting tag information for web page clustering assume that all the WebPages 
  are tagged which is a somewhat restrictive assumption. In a more realistic setting, 
  one can only expect that the tags will be available for only a small number 
  of WebPages. Researchers propose a new web page grouping approach based on Probabilistic 
  Latent Semantic Analysis (PLSA) Model. An iterative set of rules based on maximum 
  likelihood principle is employed to overcome the aforementioned computational 
  shortcoming.
    Keywords: Probabilistic latent semantic analysis;singular value decomposition;term-frequency;web page clustering;India