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Web Structure Mining: a Comparative Analysis of Hits Algorithm

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Web Structure Mining: a Comparative Analysis of Hits Algorithm
Web Structure Mining: A Comparative Analysis of HITS Algorithm
Mrs. Charmy Patel#1, Mrs. Kinjan Chauhan#2 and Mrs. Priti Patel#3
#Shree Ramkrishna Institute of Computer Education and Applied Sciences,
M.T.B College Campus, Athwalines,
Surat, Gujarat, India.
1charmyspatel@gmail.com
2Kinjanchauhan99@gmail.com
3priti_patel22@hotmail.com

Abstract: Today the amount of data available online is increasing widely. the World Wide Web has becoming one of the most valuable resources for information retrievals and knowledge discoveries. Web mining technologies are the right solutions for knowledge discovery on the Web. The knowledge extracted from the Web can be used to raise the performances for Web information retrievals, question answering, and Web based data warehousing. In this paper, we provide an introduction of Web mining as well as a review of the Web mining categories. But we focus on one of the category called the Web structure mining.
Two page ranking algorithms, HITS and PageRank, are commonly used in web structure mining. Both algorithms treat all links equally when distributing rank scores. A comparative analysis on popular methods applied in Web structure mining algorithm, show that HITS performs better than PageRank algorithm in terms of returning larger number of relevant pages to a given query.

Keywords: Web mining, Web Structure Mining, Page Rank, HITS.

I. INTRODUCTION

The World Wide Web is today 's largest warehouse of knowledge. It is a huge, widely distributed, global source for information services, hyper-link information, access and usage information and web-site contents & organizations. With the transformation of the Web into a ubiquitous tool for .e-activities. Such as e-commerce, e-learning, e-government, e-science, its use has pervaded to the realms of day-to-day work, information retrieval and business management.

Due to the increasing amount of data available online, the World Wide Web has becoming one of the most



References: [1] M. Kobayashi, and K. Takeda, .Information Retrieval on the Web., ACM Computing Surveys, Vol. 32, No.2, June 2000. [2] R. Kosala, and H. Blockeel, .Web Mining Research: A survey., SIGKDD Explorations, Vol. 2, Issue 1, July 2000, pp. 1-15. [3] http://www.cse.iitb.ac.in/internal/techreports/reports/TR-CSE-2010-31.pdf [4] http://horicky.blogspot.com/2010/03/ [5] Data Mining Techniques – Arun K Pujari

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