CFP last date
20 December 2024
Reseach Article

A Novel Approach for Organizing Web Search Results using Ranking and Clustering

by A. K. Sharma, Neelam Duhan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 10
Year of Publication: 2010
Authors: A. K. Sharma, Neelam Duhan
10.5120/966-1343

A. K. Sharma, Neelam Duhan . A Novel Approach for Organizing Web Search Results using Ranking and Clustering. International Journal of Computer Applications. 5, 10 ( August 2010), 1-9. DOI=10.5120/966-1343

@article{ 10.5120/966-1343,
author = { A. K. Sharma, Neelam Duhan },
title = { A Novel Approach for Organizing Web Search Results using Ranking and Clustering },
journal = { International Journal of Computer Applications },
issue_date = { August 2010 },
volume = { 5 },
number = { 10 },
month = { August },
year = { 2010 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume5/number10/966-1343/ },
doi = { 10.5120/966-1343 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:53:52.638749+05:30
%A A. K. Sharma
%A Neelam Duhan
%T A Novel Approach for Organizing Web Search Results using Ranking and Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 5
%N 10
%P 1-9
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

World Wide Web is considered the most valuable place for Information Retrieval and Knowledge Discovery. While retrieving information through user queries, a search engine results in a large and unmanageable collection of documents. Web mining tools are used to classify, cluster and order the documents so that users can easily navigate through the search results and find the desired information content. A more efficient way to organize the documents can be a combination of clustering and ranking, where clustering can group the documents and ranking can be applied for ordering the pages within each cluster. Based on this approach, in this paper, a mechanism is being proposed that provides ordered results in the form of clusters in accordance with user’s query. An efficient page ranking method is also proposed that orders the results according to both the relevancy and the importance of documents. This approach helps user to restrict his search to some top documents in particular clusters of his interest.

References
  1. Naresh Barsagade, “Web usage mining and pattern discovery: A survey paper”. CSE 8331, Dec, 2003.
  2. http://www.google.com/technology/index.html.
  3. A. Spink, D. Wolfram, B.J. Jansen, T. Saracevis, “Searching the Web: The public and their queries”. Journal of the American Society for Information Science and Technology 52 (3), 2001, 226-234.
  4. R.Cooley, B.Mobasher and J.Srivastava, “Web mining: Information and pattern discovery on the World Wide Web”. In 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’97), 1997.
  5. M. H. Dunham, Companion slides for the text, “Data mining: Introductory and advanced topics”. Prentice Hall, 2002.
  6. L. Page, S. Brin, R. Motwani, T. Winograd, “The pagerank citation ranking: Bringing order to the web”. Technical report, Stanford Digital Libraries SIDL-WP-1999-0120, 1999.
  7. C. Ridings and M. Shishigin, “Pagerank uncovered”. Technical report, 2002.
  8. Wenpu Xing and Ali Ghorbani, “Weighted PageRank Algorithm”. Proceedings of the second annual conference on Communication Networks and Services Research (CNSR’04), 2004 IEEE.
  9. Neelam Duhan, A. K. Sharma, Komal Kumar Bhatia, “Page Ranking Algorithms: A Survey”. In proceedings of the IEEE International Advanced Computing Conference (IACC), 2009.
  10. Jaroslav Pokorny, Jozef Smizansky, “Page Content Rank: An approach to the Web Content Mining”.
  11. http://pr.efactory.de/e-pagerank-algorithm.shtml
  12. J. Han, M. Kamber, Data Mining: Concepts and Techniques. Academic Press, London, Morgan Kaufmann Publishers, San Francisco.
  13. O. Zamir, O. Etzioni. “Web document clustering: A feasibility demonstration”. Proceedings of the 19th International ACM SIGIR Conference on Research and Development of Information Retrieval (SIGIR'98), 46-54, 1998.
  14. Hiroyuki Toda, Ryoji Kataoka, “A search result clustering method using informatively named entities”. WIDM 2005.
  15. D. J. Lawrie and W. B. Croft, “Generating hierarchical summaries for web searches”. Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval, 2003.
  16. Taher H. Haveliwala, Aristides Gionis, Dan Klein, Piotr Indyk, “Evaluating strategies for similarity search on the Web”. WWW2002, May, 2002, Honolulu, Hawaii, USA.ACM 1-58113-449-5/02/0005.
  17. J. Kleinberg, “Authorative sources in a hyperlinked environment”. Proceedings of the 23rd annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1998.
  18. Miguel Gomes da Costa Júnior, Zhiguo Gong, “Web Structure Mining: An introduction”. Proceedings of the IEEE International Conference on Information Acquisition, 2005, China.
Index Terms

Computer Science
Information Sciences

Keywords

Document Clustering PageRank Web Mining Weighted PageRank World Wide Web