Abstract—As the number of available Web pages grows; it is become more difficult for users finding documents relevant to their interests. Clustering is the classification of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait - often proximity according to some defined distance measure. Because of the short lengths of queries, approaches based on keywords are not suitable for document clustering. This paper describes a new Web Document Clustering method that makes use of user logs which allow identifying the documents the users have selected for a query. The similarity between two queries may be deduced from the common documents the users selected for them. This research paper show that a combination of both content based and session based clustering [1] is better than using either method alone. The clustered documents are arranged based on V-Ranking. In this research work, it has been proposed to display the result in visual mode of semantic search engine using V (Visual) - Ranking algorithm and bookshelf data structure. This paper proposes a semantic web search results in visualize web graphs, representations of web structure overlaid with information and pattern tiers by providing the viewer with a qualitative understanding of the information contents.
Index Terms—Book Shelf Data Structure, Content based Clustering, Session Based clustering, Visualization, (Visual)-Ranking.
Dr. S. K. Jayanthi is with Computer Science Department as Associate Professor and Head , Vellalar College for Women (Autonomous), Erode,Tamilnadu, India(e-mail: jayanthiskp@gmail.com).
S. Prema is with Computer science Department as Asst. Prof, K.S.R. College of Arts and Science, Tiruchengode -637215, Namakkal district, Tamilnadu, India . (e-mail: prema_shanmuga@yahoo.com).
Cite: S. K. Jayanthi and S. Prema, "Web Document Clustering and Visualization Results of Semantic Web Search Engine Using V-Ranking," International Journal of Computer Theory and Engineering vol. 3, no. 3, pp. 463-467, 2011.
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