Abstract
Web mining is one of the mining technologies, which applies data mining techniques in large amount of web log data. Web navigational mining discovers users’ access patterns from web logs. This information can be used to identify the behavior of the web user. However, the web data will grow rapidly in the short time, and some of the web data may be antiquated. The user behavior may be changed when the new web data is inserted into and the old web data is deleted from web logs. Therefore, the user behavior must be re-discovered from the updated web logs. However, it is very time-consuming to re-find the users’ access patterns. Hence, many researchers pay attention to the incremental mining, which utilizes the previous mining results and finds new patterns just from the inserted or deleted part of the web logs such that the mining time can be reduced.
The present paper proposes an efficient incremental web navigational mining algorithm for discovering web navigational patterns when the user sequences are inserted into and deleted from original database. It avoids re-finding the original web navigational patterns and re-counting the original candidate sequences. It uses lattice structure to keep the previous mining results such that just new candidate sequences need to be computed. Hence, the web navigational patterns can be obtained rapidly when the navigational sequence database is updated. Besides, maximal web navigational patterns can also be obtained easily by traversing the lattice structure. The experimental results show that the present algorithm is more efficient than the other approaches.
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.
Similar content being viewed by others
References
Chen, M.S., Huang, X.M., Lin, I.Y.: Capturing User Access Patterns in the Web for Data Mining. In: Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, pp. 345–348 (1999)
Cooley, R., Mobasher, B., Srivastava, J.: Web Mining: Information and Pattern Discovery on the World Wide Web. In: Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (1997)
Chen, M.S., Park, J.S., Yu, P.S.: Efficient Data Mining for Path Traversal Patterns in a Web Environment. IEEE Transaction on Knowledge and Data Engineering 10(2), 209–221 (1998)
Cheng, H., Yan, X., Han, J.: IncSpan: Incremental Mining of Sequential Patterns in Large Database. In: Proceedings of 2004 International Conference on Knowledge Discovery and Data Mining (KDD’04), Seattle, WA (August 2004)
Lee, Y.-S., Yen, S.-J., Tu, G.-H., Hsieh, M.-C.: Web Usage Mining: Integrating Path Traversal Patterns and Association Rules. In: Proceedings of International Conference on Informatics, Cybernetics, and Systems (ICICS 2003), pp. 1464–1469 (2003)
Lee, Y.-S., Yen, S.-J., Tu, G.-H., Hsieh, M.-C.: Mining Traveling and Purchasing Behaviors of Customers in Electronic Commerce Environment. In: Proceedings of IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE 2004), pp. 227–230 (2004)
Brin, S., Motwani, R., Ullman Jeffrey, D., Shalom, T.: Dynamic itemset counting and implication rules for market basket data. In: SIGMOD 1997 (1997)
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: SIGMOD 2000, pp. 1–12 (2000)
Pei, J., Han, J., Nishio, S., Tang, S., Yang, D.: H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases. In: Proc. 2001 Int. Conf. on Data Mining (2001)
Brown, C.M., Danzig, B.B., Hardy, D., Manber, U., Schwartz, M.F.: The harvest information discovery and access system. In: Proc. 2nd International World Wide Web Conference (1994)
Frakes, W.B., Baeza-Yates, R.: Infomation Retrieval Data Structures and Algorithms. Prentice Hall, Englewood Cliffs (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Maheswara Rao, V.V.R., Valli Kumari, V. (2010). A Novel Lattice Based Research Frame Work for Identifying Web User’s Behavior with Web Usage Mining. In: Das, V.V., Vijaykumar, R. (eds) Information and Communication Technologies. ICT 2010. Communications in Computer and Information Science, vol 101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15766-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-15766-0_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15765-3
Online ISBN: 978-3-642-15766-0
eBook Packages: Computer ScienceComputer Science (R0)