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A Review on Clustering of Web Search Result

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Advances in Computing and Information Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 177))

Abstract

The over abundance of information on the web, makes information retrieval a difficult process. Today’s search engines give too many results out of which only few are relevant. A user has to browse through the result pages to get the desired result. Web search result clustering is the clustering of results returned by the search engines into meaningful groups. This paper throws light and categorizes various clustering techniques that have been applied on the web search result.

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References

  1. Carpenito, C., Osinski, S., Romano, G., Weiss, D.: A Survey of Web Clustering Engines II. ACM Computing Surveys 41(3), Article 17 (2009)

    Google Scholar 

  2. Cutting, D.R., Kager, D.R., Pedersen, J.O.: Tukey JW Scatter/gather: a cluster-based approach to browsing large document collections. In: The 15th Annual International ACM Sigir Conference on Research and Development in Information Retrieval (1992)

    Google Scholar 

  3. Wang, Y., Kitsuregawa, M.: Link Based Clustering of Web Search Results. In: Wang, X.S., Yu, G., Lu, H. (eds.) WAIM 2001. LNCS, vol. 2118, pp. 225–236. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  4. Han, J., Kamber, M.: Data Mining -Concepts and Techniques. Academic Press (2001)

    Google Scholar 

  5. Steinbach, M., Karypis, G., Kumar, M.: A Comparison of Document Clustering Techniques II. In: KDD Workshop on Text Mining (2000)

    Google Scholar 

  6. Fung, B.C.M., Wang, K., Ester, M.: Hierarchical Document Clustering (2003)

    Google Scholar 

  7. Zamir, O., Etzioni, O.: Web Document Clustering: A Feasibility Demonstration. In: Proceedings of the 21st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 46–54 (1998)

    Google Scholar 

  8. Yao, T., Li, J.: A Token-based Online Web-Snippet Clustering Approach based on Directed Probability Graph. Journal of Computational Information Systems 5(3), 1235–1244 (2009)

    Google Scholar 

  9. Branson, S., Greenberg, A.: Clustering Web Search Results Using Suffix Tree Methods. Stanford University (2009)

    Google Scholar 

  10. Janruang, J., Guha, S.: Semantic Suffix Tree Clustering. In: First IRAST International Conference on Data Engineering and Internet Technology, DEIT (2011)

    Google Scholar 

  11. Zhang, D., Dong, Y.: Semantic, Hierarchical, Online Clustering of Web Search Results. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds.) APWeb 2004. LNCS, vol. 3007, pp. 69–78. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Osinski, S.: A Concept-Driven Algorithm for Clustering Search Results. IEEE Intelligent Systems 20(3), 48–54 (2005)

    Article  Google Scholar 

  13. Mecca, G., Raunich, S., Pappalardo, A.: A New Algorithm for Clustering Search Result. Journal of Data & Knowledge Engineering 62(3) (2007)

    Google Scholar 

  14. Sha, Y., Zhang, G.: Web Search Result Clustering Algorithm based on Lexical Graph. Journal of Computational Information Systems 5(1) (2009)

    Google Scholar 

  15. Navigli, R., Crisafulli, G.: Inducing Word Senses to Improve Web Search Result Clustering. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2010)

    Google Scholar 

  16. Kleinberg, J.: Authoritative Sources In A Hyperlinked Environment. In: Proceedings of the 9th ACM-SIAM Symposium on Discrete Algorithms, SODA (1998)

    Google Scholar 

  17. Page, L., Brin, S.: Web document clustering: A feasibility demonstration. In: Proceedings of SIGIR 1998, Melbourne, Australia (1998)

    Google Scholar 

  18. Bradic, A.: Search Result Clustering via Randomized Partitioning of Query-Induced Subgraphs. Telfor Journal 1(1) (2009)

    Google Scholar 

  19. Leuski, A., Allan, J.: Improving Interactive Retrieval by Combining Ranked Lists and Clustering. In: Proceeding of RIAO (2000)

    Google Scholar 

  20. Duhan, N., Sharma, A.K.: A Novel Approach for Organizing Web Search Results using Ranking and Clustering. International Journal of Computer Applications 5(10) (2010)

    Google Scholar 

  21. Wang, Y., Kitsuregawa, M.: Link Based Clustering of Web Search Results. In: Wang, X.S., Yu, G., Lu, H. (eds.) WAIM 2001. LNCS, vol. 2118, pp. 225–236. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  22. Bekkerman, R., Zilbersteinn, S., Allan, J.: Web Page Clustering using Heuristic Search in the Web Graph. In: Proceedings of IJCAI 2007, the 20th International Joint Conference on Artificial Intelligence (2007)

    Google Scholar 

  23. Alam, M., Sadaf, K.: Web Search Result Clustering using Heuristic Search and Latent Semantic Indexing. International Journal of Computer Applications 44(15) (2012)

    Google Scholar 

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Correspondence to Mansaf Alam .

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Alam, M., Sadaf, K. (2013). A Review on Clustering of Web Search Result. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_17

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  • DOI: https://doi.org/10.1007/978-3-642-31552-7_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31551-0

  • Online ISBN: 978-3-642-31552-7

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