Usage of Web Mining in Management Research

Mrs. Shivani Chaudhary .


The World-Wide Web provides every internet citizen with access to an abundance of information. The challenge of extracting knowledge from data draws upon research in marketing, management, statistics, databases, pattern recognition, machine learning, data visualization, optimization, and high-performance computing, to deliver advanced business intelligence and web discovery solutions. Web Mining is the application of data mining to discover useful knowledge from the Web. Web mining focuses now on four main research directions related to the categories of Web data: Web content mining, Web usage mining, Web structure mining, and Web user profile mining. Web content mining discovers what Web pages are about and reveals new knowledge from them. Web usage mining concerns the identification of patterns in user navigation through Web pages and is performed for the reasons of service personalization, system improvement, and usage characterization. Web structure mining investigates how the Web documents are structured, and discovers the model underlying the link structures of WWW. Web user profile mining discovers user’s profiles based on users’ behavior on the Web. This paper provides a brief overview of the accomplishments of the field, both in terms of technologies and applications, and outlines key future research direction. 


Web mining, information retrieval, Web pages, information extraction

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Google News.

Google Inc.


T. Haveliwala. Topic-sensitive PageRank. In In Proceedings of the Eleventh International World Wide Web Conference, Honolulu, Hawaii, May 2002., 2002.

N. Imafuji and M. Kitsuregawa. Effects of maximum flow algorithm on identifying web community. In Proceedings of the fourth international workshop on Web information and data management, pages 43–48. ACM Press, 2002.

The Internet Archive Project.

J.Ghosh and J. Srivastava. Proceedings of Workshop on Web Analytics. http: //, 2001.

J.Ghosh and J. Srivastava. Proceedings of Workshop on Web Mining. http: //, 2001.

L.R. Ford Jr and D.R. Fulkerson. Maximal Flow through a network. Canadian J. Math, 8:399–404, 1956.

R.H. Katz. Pervasive Computing: It’s All About Network Services, 2002.J.M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604–632, 1999.

R. Kohavi. Mining e-commerce data: The good, the bad, and the ugly. In Foster J. Srivastava, R. Cooley, M. Deshpande, and P.N. Tan. Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explorations, 1(2):12–23, 2000.


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