A Heuristic to Capture Longer User Web Navigation Patterns
- 622 Downloads
In previous work we have proposed a data mining model to capture user web navigation patterns, which models the navigation sessions as a hypertext probabilistic grammar. The grammar’s higher probability strings correspond to the user preferred trails and an algorithm was given to find all strings with probability above a threshold. Herein, we propose a heuristic aimed at finding longer trails composed of links whose average probability is above the threshold. A dynamic threshold is provided whose value is at all times proportional to the length of the trail being evaluated. We report on experiments with both real and synthetic data which were conducted to assess the heuristic’s utility.
KeywordsSynthetic Data Mining Association Rule Dynamic Threshold Exploration Tree Navigation Pattern
Unable to display preview. Download preview PDF.
- 1.J. Borges and M. Levene. Mining association rules in hypertext databases. In Proc. of the 4th Int. Conf. on Knowledge Discovery and Data Mining, pages 149–153, New York, 1998.Google Scholar
- 2.J. Borges and M. Levene. Data mining of user navigation patterns. In Proc. of the Web Usage Analysis and User Profiling Workshop, pages 31–36, San Diego, 1999.Google Scholar
- 4.R. Cooley, B. Mobasher, and J. Srivastava. Web mining: Information and patterns discovery on the world wide web. In Proc. of the 9th IEEE Int. Conf. on Tools with Artificial Intelligence, pages 558–567, 1997.Google Scholar
- 5.R. Cooley, B. Mobasher, and J. Srivastava. Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems, 1(1):5–32, 1999.Google Scholar
- 6.G. Furnas. Generalized fisheye views. In Conf. proc. on Human factors in computing systems, pages 16–23, 1986.Google Scholar
- 7.N. Kazarinoff. Geometric Inequalities. Random House, 1961.Google Scholar
- 9.M. Perkowitz and O. Etzioni. Adaptive web sites: an AI challenge. In Proc. of 15th Int. Joint Conf. on Artificial Intelligence, pages 16–21, Nagoya, 1997.Google Scholar
- 10.M. Perkowitz and O. Etzioni. Adaptive sites: Automatically synthesizing web pages. In Proc. 15th Nat. Conf. on Artificial Intelligence, pages 727–732, 1998.Google Scholar
- 12.M. Spiliopoulou and L. Faulstich. WUM: a tool for web utilization analysis. In Proc. Int. Workshop on the Web and Databases, pages 184–203, Valencia, 1998.Google Scholar
- 14.T. Yan, M. Jacobsen, H. Garcia-Molina, and U. Dayal. From user access patterns to dynamic hypertext linking. In Proc. of the fifth Int. World Wide Web Conference, pages 1007–1014, Paris, 1996.Google Scholar
- 15.N. Zin and M. Levene. Constructing web-views from automated navigation sessions. In Proc. of the ACM Digital Libraries Workshop on Organizing Web Space, pages 54–58, Berkeley, 1999.Google Scholar