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The Action and Future of Web Mining

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The Action and Future of Web Mining
Paper ID – IC 554
The Actions and Future of Web Mining

Ms. Preety Khatri Mr. Sanjay Pachauri Mr. Ritesh Singhal (Pursuing PhD, MCA, (Pursuing PhD, M.Tech (Pursuing PhD, M.Phil, M.Phil) MBA(IT), MCSE, MSc.) M.Sc., MIT) Lecturer(IT), Coordinator PGDM Associate Professor, HOD-IT Associate Professor,HOD- . QT/OR BLS Institute of Management, Mohan Nagar, Ghaziabad (U.P.)
Abstract:
From its very beginning, the potential of extracting valuable knowledge from the Web has been quite evident. Web mining – i.e. the application of data mining techniques to extract knowledge from Web content, structure, and usage – is the collection of technologies to fulfill this potential. Web mining is the application of data mining techniques to extract knowledge from Web data, where at least one of structure (hyperlink) or usage (Web log) data is used in the mining process (with or without other types of Web data). Interest in Web mining has grown rapidly in its short existence, both in the research and practitioner communities. This paper provides a brief overview of the accomplishments of the field – both in terms of technologies and applications – and outlines key future research directions.
Keywords: Web mining, Data mining, Web, Process mining, temporal

Introduction:
Web mining is the application of data mining techniques to extract knowledge from Web data - including Web documents, hyperlinks between documents, usage logs of web sites, etc. Two different approaches were taken in initially



References: 5. C. Dembeck, P. A. Greenberg, “Amazon: Caught Between a Rock and a Hard Place”, E-Commerce Times, Spetember 8, 2000, http://www.ecommercetimes.com/perl/story/2467.html. 6. DoubleClick’s Lawsuit, http://www.wired.com/news/business/0,1367,36434,00.html 9. E. Colet, “Using Data Mining to Detect Fraud in Auctions”, DSStar, 2002. 10. E 15. J. Srivastava, R. Cooley, M. Deshpande and P-N. Tan. “Web Usage Mining: Discovery and Applications of usage patterns from Web Data”, SIGKDD Explorations, Vol 1, Issue 2, 2000. 16. K 17. L.R. Ford Jr and D.R. Fulkerson, “Maximal Flow through a network.” Canadian J. Math.,8:399-404, 1956. 18. M 19. M. Pazzani, J. Muramatsu, D. Billsus, “Syskill and Webert: Identifying Interesting Web Sites”, in Proceedings of AAAI/IAAI Symposium, 1996. 20. O 21. Pang-Ning Tan, Vipin Kumar, Discovery of Web Robot Sessions based on their Navigational Patterns, Data Mining and Knowledge Discovery, 6(1): 9-35 (2002). 22. P 23. R. Cooley, “Web Usage Mining: Discovery and Usage of Interesting Patterns from Web Data”, Ph.D. Thesis, University of Minnesota, Computer Science & Engineering, 2000. 24. T

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