Classes

• Estatística (LEIC and LEM)

• Extracção de Conhecimento (LEIC, MEI, MIETI)

• SI (LEM and LGEI)

Web Usage Mining Publications

Ph.D. Dissertation

• A Data Mining Model to Capture User Web Navigation Patterns. Department of Computer Science, University College London, London University, 2000. Supervised by Mark Levene (pdf version)

Papers

• J. Borges. Methods for modelling and predicting user web navigation. To appear in proceedings of the WTI 2008 workshop (Web and Text Intelligence 08).

• J. Borges and M. Levene. Detecting Concept Drift in Web Usage Mining, to appear in Proceedings of the Workshop on Web Mining and Web Usage Analysis (WebKDD 2008), 98-110.

• J. Borges and M. Levene. Variable Length Markov Chains for Web Usage Mining, in Encyclopedia of Data Warehousing and Mining - 2nd Edition.

• J. Borges and M. Levene. Mining Users' Web Navigation Patterns and Predicting Their Next Step, in Security Informatics and Terrorism: Patrolling the Web, NATO Science for Peace and Security Series: Information and Communication Security. Edited by: C.S. Gal, P.B. Kantor and B. Shapira. Volume 15, 45-55, 2008. Book web site.

• J. Borges and M. Levene. Evaluating Variable Length Markov Chain Models for Analysis of User Web Navigation Sessions.IEEE Transactions on Knowledge and Data Engineering, Vol 19, Issue 4, pages 441--452, April 2007.

• J. Borges and M. Levene. Testing the predictive power of variable history web usage. Soft Computing - A Fusion of Foundations, Methodologies and Applications, special issue on Web intelligence and change discovery Volume 11, Number 8 / June, 2007, pp 717-727.

• J. Borges and M. Levene. Ranking pages by topology and popularity within web sites. World Wide Web Journal (2006) 9: 301–316.

• José Borges e Mark Levene, A Clustering-Based Approach for Modelling User Navigation With Increased Accuracy, in Proceedings of the Second International Workshop on Knowledge Discovery from Data Streams (IWKDDS) in conjunction with PKDD 2005, Porto, Portugal, Outubro, 2005. Presentation.

• J. Borges and M. Levene. Generating Dynamic Higher-Order Markov Models in Web Usage Mining.in Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 05), LNAI 3721, pp 34-45. Porto, Portugal, Outubro, 2005. Presentation.

• J. Borges and M. Levene. A dynamic clustering-based Markov model for web usage mining. cs.IR/0406032. 

• J. Borges and M. Levene. An average linear time algorithm for web data mining.  International Journal of Information Technology and Decision Making, 3, (2004), 307-320.

• M. Levene, J. Borges and G. Loizou. Zipf's law for web surfers. Knowledge and Information Systems an International Journal, Vol. 3, No. 1 (2001).

• J. Borges and M. Levene. A Fine Grained Heuristic to Capture Web Navigation Patterns. SIGKDD Explorations, Volume2, Issue 1, pages 40-50, 2000. Slides of presentation given at the WWW9 workshop on Web Measurements, Metrics and Mathematical Models, Amsterdam, 2000.

• J. Borges and M. Levene. A Heuristic to Capture Longer User Web Navigation Patterns. Proceedings of the 1st International Conference on Electronic Commerce and Web Technologies (EC-Web 2000), pages 155-164. September 4-6, 2000, London-Greenwich, United Kingdom.

• J. Borges and M. Levene. Data Mining of User Navigation Patterns. Proceedings of the Workshop on Web Usage Analysis and User Profiling (WEBKDD'99),  pages 31-36. August 15,1999, San Diego, CA.

Extended version in Web Usage Mining and User Profiling, Brij Masand e Myra Spiliopoulou (Eds.), Springer, LNCS 1836, pp 92-111. series of Springer Verlag. You can download a copy of it: Data Mining of User Navigation Patterns.

• J. Borges and M. Levene. Mining Association Rules in Hypertext Databases. Proceedings of the fourth International Conference on Knowledge Discovery and Data Mining (KDD98), pages 149-153. New York, USA, August 1998.

An extended version available as Research Note RN/98/24, Department of Computer Science, University College London, March 1998.