Abstract

Web query optimization is the focus of recent research and development efforts. To fetch the required information, the users are using search engines and sometimes through the website interfaces. One approach is search engine optimization which is used by the website developers to popularize their website through the search engine results. Clustering is a main task of explorative data mining process and a common technique for grouping the web search results into a different category based on the specific web contents. A clustering search engine called Lingo used only snippets to cluster the documents. Though this method takes less time to cluster the documents, it could not be able to produce the clusters of good quality. This study focuses on clustering all documents using by applying semantic similarity between words and then by applying modified lingo algorithm in less time and produce good quality.

Keywords:

Clustering algorithm, lingo algorithm, search engine optimization, semantic web, snippet, web query optimization,

References

1. Beeferman, D. and A. Berger, 2000. Agglomerative clustering of a search engine query log. Proceeding of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Boston, pp: 407-416.
   CrossRef    
2. Brin, S. and L. Page, 1998. The anatomy of a large-scale hyper textual web search engine. Comput. Netw., 30(17): 107-117.
   CrossRef    
3. Correia-Saravia, P., E. Silva de Moura, N. Ziviani, W. Meira, R. Fonseca and B. Ribeiro-Neto, 2001. Rank-preserving two-level caching for scalable search engines. Proceeding of the 24th International ACM Conference on Research and Development in Information Retrieval. New Orleans, LA, pp: 51-58.
   
4. Hongwei, Y., 2010. A document clustering algorithm for web search engine retrieval system. Proceeding of the International Conference on e-Education, e-Business, e-Management and e-Learning, pp: 383-386.
   
5. Kantabutra, S., 2001. Efficient representation of cluster structure in large data sets. Ph.D. Thesis, Tufts University, Medford, MA.
   
6. Kleinberg, J., 1998a. Authoritative sources in a hyperlinked environment. Proceeding of the ACM-SIAM Symposium on Discrete Algorithms.
   
7. Kleinberg, J.M., 1998b. Authoritative sources in a hyperlinked environment. J. ACM, 46(5): 604-632.
   CrossRef    
8. Lawrence, S. and C.L. Giles, 1999. Searching the web: General and scientific information access. IEEE Commun. Mag., 37(1): 116-122.
   CrossRef    
9. Minky, J. and K. Nisha, 2013. K-means clustering technique on search engine dataset using data mining tool. Int. J. Inform. Comput. Technol., 3(6): 505-510.
   
10. Ricardo, B.Y., H. Carlos and M. Marcelo, 2007. Improving search engines by query clustering. J. Am. Soc. Inform. Sci. Technol., 58(12): 1793-1804.
    CrossRef    
11. Wagsta, K. and C. Cardie, 2000. Clustering with instance-level constraints. Proceeding of the 17th International Conference on Machine Learning. Palo Alto, Morgan Kaufmann, CA, pp: 1103-1110.
    
12. Wang, J. and Z.Z. OuYang, 2010. The research of K-means clustering algorithm based on association rules. Proceeding of the International Conference on Challenges in Environmental Science and Computer Engineering, pp: 285-286.
    
13. Wen, J., J. Nie and H. Zhang, 2001. Clustering user queries of a search engine. Proceeding of the International Conference on World Wide Web. Hong Kong, China, pp: 162-168.
    CrossRef    
14. Zhang, D. and Y. Dong, 2002. A novel web usage mining approach for search engines. Comput. Netw., 39(3): 303-310.
    CrossRef    
15. Zhao, Y. and G. Karypis, 2004. Empirical and theoretical comparisons of selected criterion functions for document clustering. Mach. Learn., 55(3).
    CrossRef    
16. Zhexue, H., 1998. A fast clustering algorithm to cluster very large categorical data sets in data mining. Cooperative Research Centre for Advanced Computational Systems (ACSys) Established under the Australian Government’s Cooperative Research Centres Program.
    

Competing interests

The authors have no competing interests.

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