efficient page ranking using fuzzy logic based weighted page content rank algorithm
VinothKumar.G.S,Janet.J,Kamal.N
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:2 Issue:3 Year: 08 April,2014 Pages:1-14
Abstract
In today’s world, Web Mining is a much sought after application in the field of Data Mining which helps us in information retrieval in the World Wide Web. Many search engines rank their search results in response to the queries of the users. It includes Link Analysis algorithms i.e., Page Rank, Weighted Page Rank and Weighted Page Content Rank. The most commonly used algorithm in Web mining is the Page Ranking algorithm. Weighted Page Rank also takes the importance of the in links and out links of the pages but the rank score to all links is not equally distributed .i.e. it results in unequal distribution. Weighted Page Content Rank based on web content mining and structure mining that shows the relevancy of the pages to a given query is better determined, as compared to the existing Page Rank and Weighted Page Rank algorithms. Weighted Page Content Rank algorithm has been experimented and it was found out that the time taken to process the web page is more in that algorithm. The processing time increases considerably with the increase in the number of Web pages. In this paper, we have proposed a novel Fuzzy Logic based Web page Content Rank (FLWPCR) algorithm for effective page ranking. The results prove that this hybrid algorithm improves the performance of page ranking in terms of decreased time in the processing of Web pages.
Kewords
Web Mining, Page Rank, Weighted Page Rank, Weighted Page Content Rank, Fuzzy K Mean, Fuzzy C Mean.
Reference
1. Chintan deepkaur, Rinkle Rani Aggarwal “Web Mining Tasks and Types: A Survey” Volume2, Issue 2, Feb.2012. 2. Neelam Tyagi, Simple Sharma, “Weighted Page rank algorithm based on number of visits of Links of web page “International Journal of Soft Computing and EngineeringVolume2, Issue-3,July2012. 3.Laxmi Choudhary and Bhawani Shankar Burdak, “ Role of Ranking Algorithms for Information Retrieval “International Journal of Artificial Intelligence & Applications(IJAIA),Volume3, No.4, July2012. 4. T.Munibalaji, C.Balamurugan “Analysis of Link algorithms for Web Mining” International Journal of Engineering and Innovative Technology, Volume1, Issue 2, Feb2012. 5. R.Krishnapuram , A.Joshi, O.Nasraoui, L.Yi, “Low complexity fuzzy, relational clustering algorithms for web mining” IEEE Trans. Fuzzy systems 9.595-607 Aug.2011. 6. Tamana Bhatia, “Link analysis Algorithms for Web Mining” IJCST Volume2, Issue2, June2011. 7. RekhaJain, Dr.G.N. Purohit “Page Ranking Algorithms for Web Mining” International Journal of Computer Applications Volume 13, No.5, Jan 2011 8. J.C.Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms Plenum Press, New York, Volume 3, June 1981. 9. Vaibhav C.Naik “Fuzzy C-Means Clustering Approach” Department of Industrial land Management Systems Engineering, Univeristy of South Florida, Volume2, 8 July, 2004.
