Abstract
Web mining is a broad term that has been used to refer to the process of information discovery from Web sources: content, structure, and usage. Information collected by web servers and kept in the server log is the main source of data for analyzing user navigation patterns. Notwithstanding, knowing the most frequent user paths is not enough: it is necessary to integrate web mining with the company site goals in order to make sites more competitive. The concept of Web Goal Mining is introduced in this paper to refer to the process information discovery of the relationship between site visitors and sponsor goals.
Research has been partially supported by Programa de Desarrollo Tecnológico (Uruguay).
Research has been partially supported by UPM (Spain) under project WEBP-RT.
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References
Spiliopoulou M, Pohle C. Data Mining for Measuring and Improving the Success of Web Sites. Data mining and Knowledge Discovery, (5)1–2, 2001
Han J. and Kamber M. (2001) Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers.
Berthon P., Pitt L., Watson R. The World Wide Web as an advertising medium. Journal of Advertising Research 36(1) pp. 43–54, 1996
Eighmey J. On the Web: It’s What You Say and How You Say It. Greenlee School of Journalism and Communication, Iowa State University http://www.Eighmey.Website.Response.Profile.htm
The CRM Handbook Jill Dyché — Addison Wesley 2001
Hu X., Cercone N. An OLAM Framework for Web Usage Mining and Business Intelligence Reporting-WCCI 2002
Menasalvas E. Hochsztain E. Sessions Value as measure of web site goal achievement. SNPD’2002. Madrid July 2002
Kimball R. Merz, R. The Data Webhouse Toolkit Wiley, 2000
Spiliopoulou M., Pohle C., Faulstich L. (1999) Improving the efectiveness of a web site with web usage mining. In Proceedings WEBKDD99.
Menasalvas E, Millán S, Hochsztain E. (2002) A Granular Approach for Analyzing the Degree of Afability of a Web Site. In International Conference on Rough Sets and Current Trends in Computing RSCTC2002
Mobasher B., Jain N., Han E. and Srivastava J. (1997) Web mining: Pattern discovery from WWW transaction. In Int Conference on Tools with Artificial Intellgence, pages 0558–567, New Port.
Srivastava J., Cooley R., Deshpande M., and Pang-Ning Tan. (2000) Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations, 1:12–23.
Florescu D., Levy A., and Mendelzon A. (1998) Database techniques for the World-Wide Web: A survey. SIGMOD Record (ACM Special Interest Group on Management of Data), 27(3):59.
Menasalvas E, Millán S, Hadjimichael M, Hochsztain E (2002) An algorithm to calculate the expected value of an ongoing user session In The 2002 IEEE Data Mining Conference ICDM’ 02
Shahabi C., Zarkesh A.M., Adibi J., and Shah V. (1997) Knowledge discovery from user’s web-page navigation. In Proceedings of the Seventh International Workshop on Research Issues in Data Engineering, High Performance Database Management for Large-Scale Applications (RIDE’97), pages 20–1, Washington-Brussels-Tokyo, IEEE.
Nasraoiu O., Krisnapuram R., and Joshi A. Mining web access logs using a fuzzy relational clustering algorithm based on a robust estimator.
Mobasher B., Dai H., Luo T., Nakagawa M., and Witshire J.. (2000) Discovery of aggregate usage profiles for web personalization. In Proceedings of the WebKDD Workshop.
Borges J. and Levene M. (2000) A heuristic to capture longer user web navigation patterns. In Proc. Of the First International Conference on Electronic Commerce and Web Technologies, Greenwich, U.K., September.
Pei J., Han J., Mortazavi-Asl B., and Zhu H. (2000) Mining access patterns eficiently from web logs. In Proceedings Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’00).
Wolfang G. and Lars S. Mining web navigation path fragments. In Workshop on Web Mining for E-Commerce-Challenges and Opportunities Working Notes (KDD2000), pages 105–110, Boston, MA, August 2000.
Ansari S. and Kohavi R. and Mason L. and Zheng Z. Integrating E-commerce and Data Mining: Architecture and Challenges. In Workshop on Web Mining for E-Commerce — Challenges and Opportunities Working Notes (KDD2000), pages, Boston, MA, August 2000.
Spiliopoulou M., Mobase B., Berent B. and Nakagawa M. A Framework for the evaluation of session reconstruction heuristics and web Usage Analysis, in I. Journal on Computing pp 1–20, 2002
Berent B. Detail and Context in Web Usage Mining: Coarsing and Visualazing Sequences, In Proc. WEBKDD 2001
Mobasher B. Integrating Web Usage and Content Mining for More Effective Personalization, In Proc. ECWeb 2000
Department of Defense, Framework for Managing Process Improvement, On-line guide: http://www.c3i.osd.mil/bpr/bprcd/policy/fr_toc.html. Dec 2001
Mobasher B., Berent B., Spiliopoulou M. and Wiltshire J. Measuring the Accuracy of Sessionizers for Web Usage Analysis. In Proceedings of the Web Mining Workshop at the First SIAM International Conference on Data Mining, April 2001
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Hochsztain, E., Millán, S., Pardo, B., Peña, J.M., Menasalvas, E. (2003). A Framework to Integrate Business Goals in Web Usage Mining. In: Menasalvas, E., Segovia, J., Szczepaniak, P.S. (eds) Advances in Web Intelligence. AWIC 2003. Lecture Notes in Computer Science, vol 2663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44831-4_4
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