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Visualizing web site comparisons
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Source International World Wide Web Conference archive
Proceedings of the 11th international conference on World Wide Web table of contents
Honolulu, Hawaii, USA
SESSION: Browsing table of contents
Pages: 693 - 703  
Year of Publication: 2002
ISBN:1-58113-449-5
Authors
Bing Liu  National University of Singapore/Singapore-MIT Alliance, Singapore
Kaidi Zhao  National University of Singapore/Singapore-MIT Alliance, Singapore
Lan Yi  National University of Singapore/Singapore-MIT Alliance, Singapore
Sponsors
ACM: Association for Computing Machinery
: WWW'02
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 13,   Downloads (12 Months): 87,   Citation Count: 10
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ABSTRACT

The Web is increasingly becoming an important channel for conducting businesses, disseminating information, and communicating with people on a global scale. More and more companies, organizations, and individuals are publishing their information on the Web. With all this information publicly available, naturally companies and individuals want to find useful information from these Web pages. As an example, companies always want to know what their competitors are doing and what products and services they are offering. Knowing such information, the companies can learn from their competitors and/or design countermeasures to improve their own competitiveness. The ability to effectively find such business intelligence information is increasingly becoming crucial to the survival and growth of any company. Despite its importance, little work has been done in this area. In this paper, we propose a novel visualization technique to help the user find useful information from his/her competitors' Web site easily and quickly. It involves visualizing (with the help of a clustering system) the comparison of the user's Web site and the competitor's Web site to find similarities and differences between the sites. The visualization is such that with a single glance, the user is able to see the key similarities and differences of the two sites. He/she can then quickly focus on those interesting clusters and pages to browse the details. Experiment results and practical applications show that the technique is effective.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Allan, J., Leouski, A. V. and Swan, R. C. "Interactive Cluster Visualization for Information Retrieval". Tech. Rep. IR-116, Uni. of Mass., Amherst, 1997.
 
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Ashish, N. and Knoblock, C. "Wrapper Generation for Semi-structured Internet Sources". Workshop on Management of Semistructured Data, Ventana Canyon Resort, Tucson, Arizona. 1997.
 
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Brown, M. H., Marais, H., Najork, M. A. and Weihl, W. E. "Focus+Context Displays of Web Pages: Implementation Alternatives". WWW-6. 1997.
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Carey, M., Kriwaczek, F. and Ruger, S. M. "A Visualization Interface for Document Searching and Browsing". Proc of NPIVM 2000, 2000.
 
8
 
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Chen, Y. F. and Koutsofios, E. "WebCiao: A Website Visualization and Tracking System." WebNet97, 1997.
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12
 
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Fu, Y., Sandhu, K. and Shih, M Y. "Clustering of Web Users Based on Access Patterns." In Proceedings of the 1999 KDD Workshop on Web Mining. 1999.
 
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Mendelzon, A., Mihaila, G. and Milo, T. "Querying the World Wide Web." International Journal on Digital Libraries, 1(1):54--67, 1997.
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Piatesky-Shapiro, G. and Matheus, C. "The Interestingness of Deviations". KDD-94. 1994.
 
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Steinbach, M., Karypis, G. and Kumar, V. "A Comparison of Document Clustering Techniques". In KDD Workshop on Text Mining, 2000.
 
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CITED BY  10