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Cluster Based Web Search Using Support Vector Machine
Rita. S. Shelke, Devendra Singh Thakore
Pages - 134 - 158     |    Revised - 31-03-2011     |    Published - 04-04-2011
Volume - 5   Issue - 1    |    Publication Date - March / April 2011  Table of Contents
SVM, ER, Cluster
Now days, searches for the web pages of a person with a given name constitute a notable fraction of queries to Web search engines. This method exploits a variety of semantic information extracted from web pages. The rapid growth of the Internet has made the Web a popular place for collecting information. Today, Internet user access billions of web pages online using search engines. Information in the Web comes from many sources, including websites of companies, organizations, communications and personal homepages, etc. Effective representation of Web search results remains an open problem in the Information Retrieval community. For ambiguous queries, a traditional approach is to organize search results into groups (clusters), one for each meaning of the query. These groups are usually constructed according to the topical similarity of the retrieved documents, but it is possible for documents to be totally dissimilar and still correspond to the same meaning of the query. To overcome this problem, the relevant Web pages are often located close to each other in the Web graph of hyperlinks. It presents a graphical approach for entity resolution & complements the traditional methodology with the analysis of the entity-relationship (ER) graph constructed for the dataset being analyzed. It also demonstrates a technique that measures the degree of interconnectedness between various pairs of nodes in the graph. It can significantly improve the quality of entity resolution. Using Support vector machines (SVMs) which are a set of related Supervised learning methods used for classification of load of user queries to the sever machine to different client machines so that system will be stable. clusters web pages based on their capacities stores whole database on server machine. Keywords: SVM, cluster; ER.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 iSEEK 
5 Socol@r  
6 Scribd 
7 WorldCat 
8 SlideShare 
9 PdfSR 
1 D.V.Kalashnikov, S.Mehrotra, R.N.Turen and Z.Chen, “Web People Search via Connection Analysis” IEEE Transactions on Knowledge and data engg.Vol 20, No11, Novr 2008.
2 D.V. Kalashnikov, S. Mehrotra, Z. Chen, R. Nuray-Turan, and N.Ashish, “Disambiguation Algorithm for People Search on the Web,” Proc. IEEE Int’l Conf. Data Eng. (ICDE ’07), Apr. 2007.
3 M. F. Porter. An algorithm for suffix stripping. Program Vol. 14, no. 3, pp 130-137.
4 S. l. Osinski, J. Stefanowski, and D. Weiss. “Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition”.
5 Z. Dong. “Towards Web Information Clustering”. PhD thesis, Southeast University, Nanjing, China, 2002.
6 S. l. Osinski. “An Algorithm for Clustering of Web Search Results”. Master’s thesis, Pozna´n University of Technology, Poland, 2003.
7 G. Salton. “Automatic Text Processing — The Transformation, Analysis,and Retrieval of Information by Computer”. Addison–Wesley, 1989.
8 O. E. Zamir. “Clustering Web Documents: A Phrase-Based Method for Grouping SearchEngine Results”. Doctoral Dissertation, University of Washington, 1999.
9 J. Stefanowski and D. Weiss. “Web search results clustering in Polish- Advances in Soft Computing, Intelligent Information Processing and Web Mining”, Proceedings of the International IIS: IIPWM´03 Conference,Zakopane, Poland, vol. 579 (XIV), 2003, pp. 209-22.
Mr. Rita. S. Shelke
- India
Dr. Devendra Singh Thakore
- India