@article{471,
  author = {S. Poomagal, T. Hamsapriya},
  title = {Knee finding based Optimized K-Means for Web Search},
  journal = {International Journal of Web Applications},
  year = {2011},
  volume = {3},
  number = {2},
  doi = {},
  url = {http://www.dline.info/ijwa/fulltext/v3n2/1.pdf},
  abstract = {With the vast amount of information available online, searching results for a given query requires the user to go through many titles and snippets. This searching time can be reduced by clustering search results into clusters so that the user can select the relevant cluster at a glance by looking at the cluster labels. For web page clustering, terms (features) can be extracted from different parts of the web page. Giansalvatore, Salvatore and Alessandro [1] have extracted terms from entire web page for clustering. Number of terms returned in this case is more and it produces lengthy vectors. To reduce the size of the vector, Stanis law Osinski et al.,[2] and Ahmed Sameh and Amar Kadray [3] have considered terms from the snippets. In this work, we extracted terms from Uniform Resource Locator (URL), Title tag and Meta tag and we used optimized Kmeans algorithm for clustering. Optimization of K-means algorithm is done by selecting the number of clusters using knee finding algorithm instead of selecting it randomly. We compared our method with existing methods in terms of Intra-cluster distance and Inter-cluster distance.},
}