Study and Analysis of Page Ranking Algorithms in Web Structure Mining

  IJCOT-book-cover
 
International Journal of Computer & Organization Trends  (IJCOT)          
 
© 2016 by IJCOT Journal
Volume - 6 Issue - 3
Year of Publication : 2016
Authors : RenuKumari, Mamta Yadav
  10.14445/22492593/IJCOT-V34P302

MLA

RenuKumari, Mamta Yadav "Image & Video Quality Assessment and Human Visual Perception", International Journal of Computer & organization Trends (IJCOT), V6(3):5-9 May - Jun 2016, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.

Abstract The World Wide Web consists billions of web pages and huge amount of information available within the web pages. To retrieve required information from World Wide Web, search engines perform number of tasks based on their respective architecture and use various ranking algorithms for getting the desired result. To support the users to navigate in the result list, various ranking methods are applied on the search results. Some basic algorithms are Page Rank Algorithm, Weighted Page Rank Algorithm, and HITS. All these algorithms are used to discover more relevant pages on the top of the result-list This new ranking mechanism is known as Weighted Page Rank Algorithm based on Visits of Links (VOL).The original Weighted Page Rank algorithm (WPR) takes into account the importance of both the in links and out links of the web pages and distributes rank scores based on the popularity of the pages. The original Weighted Page Rank Algorithm is based on the popularity (importance) of in links and out links of a hyperlinked web graph. It calculates relevancy of a web page higher than the standard Page Rank algorithm, which is used by famous search engine Google. This technique increases the relevancy score than the existing one. So, this concept is very useful to display most valuable pages on the top of the result list on the basis of user’s browsing behavior, which reduce the search space to a large scale.

References-

1) ShotaHatakenaka, Takao Miura, “Ranking Documents using Similarity- based Page Ranks”, 2011 IEEE, 978-1-4577- 0251-8.
2) Gyanendra Kumar, NeelamDuahn, and Sharma A. K., “Page Ranking Based on Number of Visits of Web Pages”, International Conference on Computer & Communication Technology (ICCCT)-2011, 978-1-4577-1385-9.
3) Lili Yan, Yingbin Wei, ZhanjiGui, and Chen Yizhuo, “Research on Page Rank and Hyperlink-Induced Topic Search in Web Structure Mining”, 2011 IEEE, 978-1-4244- 7255-0.
4) Chongchong Zhao, Zhiqiang Zhang, Hualong Li, and XieXiaoqin, “A Search Result Ranking Algorithm Based on Web Pages and Tags Clustering”, 978-1-4244-8728-8, 2011 IEEE.
5) M. Sathya, J. Jayanthi, and Basker N., “Link Based K-Means Clustering Algorithm for Information Retrieval”, 2011 IEEE, 978-1-4577-0590-8.
6) P Ravi Kumar, and Singh Ashutoshkumar, “Web Structure Mining Exploring Hyperlinks and Algorithms for Information Retrieval”, American Journal of Applied Sciences, 7 (6) 840- 845 2010.
7) D.K. Sharma, and Sharma A.K., “A Comparative Analysis of Web Page Ranking Algorithms”, International Journal on Computer Science and Engineering Vol. 02-08, 2010, pp. 2670-2676
8) N. Duhan, A. K. Sharma and Bhatia K. K., “Page Ranking Algorithms: A Survey, Proceedings of the IEEE International Conference on Advance Computing, 2009, 978-1-4244-1888- 6.
9) C.D. Manning, P. Raghavan, and Schutze, H., “ Introduction to Information Retrieval”, Cambridge University Press, 2008
10) Wen-ChihPeng and Lin Yu-Chin, “Ranking Web Search Results from Personalized Perspective”, Proceedings of the 8th IEEE International Conference on E-Commerce Technology and the 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/EEE’06), 0-7695-2511-3.
11) Michael Brinkmeier,” Page Rank revisited”, [J], ACM Transactions on Internet Technology, 2006, l6 (3): 282 - 301
12) A.N. Langville, and Meyer, C.D., “Google’s Page Rank and Beyond: The Science of Search Engine Rankings”, Princeton University Press, June 2006
13) Yiqun Liu, Min Zhang, and RuLiyun, “Automatic Query Type Identification Based on Click Through Information”, Asia Information Retrieval Symposium(AIRS), 2006.
14) M. G. da, Gomes Jr. and Gong Z., “Web Structure Mining: An Introduction”, Proceedings of the IEEE International Conference on Information Acquisition, 2005.
15) Zhang M, Ma SP, and Song RH, “DF or IDF: On the use of primary feature model for Web information retrieval”. Journal of Software, 2005, 5(16):1012-1020.
16) Guo Yan, BaiShuo, Yang Zhi-feng, and Zhang Kai, “Analyzing Scale of Web Logs and Mining Users’ Interests”, [J], Chinese Journal of Computers, 2005, 9(28):1483-1496.

Keywords-
WWW, PAGE, Techniques, PRA, WPR, HITS.