The week's pick
Random Articles
Reseach Article
Ranking of Web Documents for Domain Specific Database
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 135 - Number 6 |
| Year of Publication: 2016 |
| Authors: Ginni Aggarwal, Mukesh Rawat |
10.5120/ijca2016908364
|
Ginni Aggarwal, Mukesh Rawat . Ranking of Web Documents for Domain Specific Database. International Journal of Computer Applications. 135, 6 ( February 2016), 16-18. DOI=10.5120/ijca2016908364
Abstract
Now a days, search engines are been most widely used for extracting information from various resources throughout the world. This paper proposed an idea for ranking of web documents offline by mapping the search query terms and the keywords coming in the documents. This paper proposes a new and efficient methodology for indexing of web documents. This technique provide relevant results to the user according to their query. This paper provide better result in retrieving related documents after removing the cue words and frequent used words so, the time will be reduced for finding the appropriate document.
References
- Jayanthi Manicassamy et al /International Journal on Computer Science and EngineeringVol.1(2),2009,111-115
- R. Agrawal, C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm for generation of frequent item sets. Journal of Parallel and Distributed Computing, 61(3):350–371, 2001.
- M. Ankerst, M. Breunig, H. Kriegel, and J. Sander. Optics: Ordering points to identify the clustering structure. In 1999 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’99), pages 49–60, Philadelphia, PA, June 1999.
- F. Beil, M. Ester, and X. Xu. Frequent term-based text clustering. In Proc. 8th Int. Conf. on Knowledge Discovery and Data Mining (KDD)’2002, Edmonton, Alberta, Canada, 2002.http://www.cs.sfu.ca/˜ ester/publications.html.
- S. Chakrabarti. Data mining for hypertext: A tutorial survey. SIGKDD Explorations: Newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining, ACM, 1:1–11, 2000.
- P. Domingos and G. Hulten. Mining high-speed data streams. In Knowledge Discovery and Data Mining, pages 71–80, 2000.
- R. C. Dubes and A. K. Jain. Algorithms for Clustering Data. Prentice Hall College Div, Englewood Cli®s, NJ, March 1998.
- S. Guha, N. Mishra, R. Motwani, and L. O’Callaghan. Clustering data streams. In IEEE Symposium on Foundations of Computer Science, pages 359–366, 2000.
- R. Agrawal, C. Aggarwal, and V. V. V. Prasad. Depth-first generation of large item sets for association rules. Technical Report RC21538, IBM Technical Report, October 1999.
- M. Charikar, C. Chekuri, T. Feder, and R. Motwani. Incremental clustering and dynamic information retrieval. In Proceedings of the 29th Symposium on Theory of Computing STOC 1997, pages 626–635, 1997.
Index Terms
Keywords