Abstract
This chapter discusses data-mining techniques for web-related data. In particular, we discuss techniques that can help information seekers locate relevant information on web. Two kinds of techniques, web-structure mining and web-log mining, are discussed. We also examine three techniques, authorities and hubs [10], anchor points [9], and PageRank [13] that examine the link structures of hypertext web pages. Since the web is huge and dynamic, it is not possible for any IR system to maintain a global view of the web. Recommendation of web information, therefore, has to be based on incomplete information. We discuss the idea of Internet GlOSS [2], which uses word statistics to make intelligent guess on the topics of interest of web sites. Also we discuss how the interest of web users can be abstracted in user profiles. Understanding both web users and web sites allows an effective matching of the two. Finally, we explain how mining web-log data can discover the topics of interest of web sites and user profiles.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
V.N. Gudivada. Information Retrieval on the World Wide Web. IEEE Internet Computing, Vol. 1, No. 5, 1997, pp. 58–68.
C.Y. Ng, Ben Kao, David Cheung. Text-Source Discovery and GlOSS Update in a Dynamic Web. Proceedings of the Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2000.
W. Frakes, R. Baeza-Yates. Information Retrieval — Data Structures and Algorithms. Prentice-Hall, 1992.
David Cheung, Ben Kao, Joseph Lee. Discovering User Access Patterns on the World-WideWeb, in Knowledge Based Systems Journal, Elsevier Science, V10, N7, May 1998.
A. Tomasic, L. Gravano, and H. Garcia-Molina. The Effectiveness of GlOSS For the Text-Database Discovery Problem, Proceedings of the 1994 ACM SIGMOD, 1994.
The Web Robots FAQ. http://info.webcrawler.com/mak/projects/robots/faci.html
S. Feldman. Just the Answers, Please: Choosing a Web Search Service, The Magazine for Database Professionals, May 1997.
Ben Kao, Joseph Lee, C.Y. Ng, and David Cheung. Anchor Point Indexing in Web Document Retrieval, in IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 30(3), pp. 364–373, 2000.
J.M. Kleinberg. Authoritative Sources In a Hyperlinked Environment, Journal of the ACM, 46, 1999.
B. Grossan. Search Engines: What They Are? How They Work? http://webreference.com/content/search/features.html
J. Nielsen. The Art of Navigating Through Hypertext. Communications of the ACM, 33 (3): 297–310, 1990.
L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank Citation Ranking: Bringing Order to the Web. Technical report, Computer Systems Laboratory, Stanford University, 1998.
R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Trawling the Web for Emerging Cyber-communities. Proceedings of the Eighth International Conference on the Web-WideWeb, 1999.
J. Dean and M.R. Henzinger. Finding Related Pages in the World-Wide-Web. Proceedings of the Eighth International Conference on the Web-Wide-Web, 1999.
D.L. Lee et. al. Document ranking and the Vector-Space Model. IEEE Software, Vol. 14, No. 2, Mar/Apr 1997, 67–75.
G. Salton. Automatic text processing: the transformation, analysis, and retrieval of information by computer. Mass: Add-Wesley, 1989.
L. Gravano et. al. The Efficacy of GlOSS for the Text Database Discovery Problem. ACM SIGMOD’94, 1994.
L. Gravano et. al. Precision and Recall of GlOSS Estimators for Database Discovery. PDIS’94, 1994.
L. Gravano et. al. Generalizing GLOSS to Vector-Space Databases and Broker Hierarchies. VLDB’95, May 1995.
J. Hartigan. Clustering Algorithms. Wiley, New York, 1975.
A. Arasu, J. Cho, H. Garcia-Molina, A. Paepcke and S. Raghavan. Searching the Web. ACM Transactions on Internet Technology, Vol. 1, No. 1, Aug 2001, 2–43.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kao, B., Cheung, D. (2003). Information Discovery on the World-Wide-Web. In: Feng, D.D., Siu, WC., Zhang, HJ. (eds) Multimedia Information Retrieval and Management. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-05300-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-662-05300-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05533-1
Online ISBN: 978-3-662-05300-3
eBook Packages: Springer Book Archive