Ranking Pages of Clustered Users using Weighted Page Rank Algorithm with User Access Period

Ranking Pages of Clustered Users using Weighted Page Rank Algorithm with User Access Period

G. Sumathi (Department of Information Science and Technology, Anna University, Chennai, India), S. Sendhilkumar (Department of Information Science and Technology, Anna University, Chennai, India) and G.S. Mahalakshmi (Department of Computer Science and Engineering, Anna University, Chennai, India)
Copyright: © 2015 |Pages: 21
DOI: 10.4018/IJIIT.2015100102
OnDemand PDF Download:
No Current Special Offers


The World Wide Web comprises billions of web pages and a tremendous amount of information accessible inside of web pages. To recover obliged data from the World Wide Web, search engines perform number of tasks in light of their separate structural planning. The point at which a user gives a query to the search engine, it commonly returns a bulky number of pages related to the user's query. To backing the users to explore in the returned list, different ranking techniques are connected on the search results. The vast majority of the ranking calculations, which are given in the related work, are either link or content based. The existing works don't consider user access patterns. In this paper, a page ranking approach of Weighted Page Rank Score Algorithm taking user access is being conceived for search engines, which deals with the premise of weighted page rank method and considers user access period of web pages into record. For this reason, the web users are clustered based on the Particle Swarm Optimization (PSO) approach. From those groups, the pages are ranked by improving the weighted page rank approach with usage based parameter of user access period. This calculation is utilized to discover more applicable pages as per user's query. In this way, this idea is extremely helpful to show the most important pages on the uppermost part of the search list on the principle of user searching behavior, which shrinks the search space on a huge scale.
Article Preview

2. Web Mining

Extraction of useful (non-inconsequential, understood, already obscure and conceivably valuable) information or patterns from substantial databases is called Data Mining. Web Mining is the application of data mining approach to find and recover valuable information and patterns from the World Wide Web archives and services web mining can be partitioned into three classes (Srivastava et al., 2005):

  • Web Content Mining

  • Web Structure Mining

  • Web Usage Mining

2.1. Web Content Mining (WCM)

WCM depicts the automatic search of data resources accessible on the web, and includes mining web information content. It is accentuation on the content of the site page not its hyperlinks. It can be connected on site pages itself or on the outcome pages got from a web search engine. WCM is separated from two distinct perspectives: Information Retrieval (IR) View and Database View. In IR view, the greater part of the researches utilization pack of words, which is in light of the insights about single words in isolation, to represent unstructured content. For the semi-organized information, every one of the works use the HTML structures insides the documents. For database perspective, Web mining dependably tries to gather the structure of the web page to change a web site to turn into a database.

Complete Article List

Search this Journal:
Open Access Articles: Forthcoming
Volume 18: 4 Issues (2022): 2 Released, 2 Forthcoming
Volume 17: 4 Issues (2021)
Volume 16: 4 Issues (2020)
Volume 15: 4 Issues (2019)
Volume 14: 4 Issues (2018)
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing