Email this article 
Open Access
Subscription or Fee Access
Prediction of Web Users Browsing Behaviour Using Fast Longest Common Sub-Sequence
Abstract
As the Web and its usage continues to grow, so grows the opportunity to analyze Web data and extract all manner of useful knowledge from it. The past nine years have seen the emergence of Web mining as a rapidly growing area, due to the efforts of the research community as well as various organizations that are practicing it. The various works proposed in this area with particular emphasize on web usage mining. In the present work, the application of clustering to extract user navigation behaviour pattern is probed and the methods and techniques used are explained in the Methodology. Experiments were conducted on a Pentium IV system with 512MB memory, running in Windows environment. The application was developed in MATLAB 7.3. The results of this study are divided into the following sections: Preprocessing results, Pattern Discovery and Performance Analysis.
Keywords
References
Joachims, T., Freitag, D., & Mitchell, T. (1997, August). Webwatcher: A tour guide for the World Wide Web. In IJCAI (1) (pp. 770-777).
Fawcett, T., & Provost, F. (1997). Adaptive fraud detection. Data mining and knowledge discovery, 1(3), 291-316.
Srivastava, J., Cooley, R., Deshpande, M., & Tan, P. N. (2000). Web usage mining: Discovery and applications of usage patterns from web data. AcmSigkdd Explorations Newsletter, 1(2), 12-23.
Zhang, Z., Huang, L., Shulmeister, V. M., Chi, Y. I., Kim, K. K., Hung, L. W., ... & Kim, S. H. (1998). Electron transfer by domain movement in cytochrome bc1. Nature, 392(6677), 677-684.
Cooley, R., Mobasher, B., & Srivastava, J. (1999). Data preparation for mining World Wide Web browsing patterns. Knowledge and information systems, 1(1), 5-32.
Shahabi, C., & Banaei-Kashani, F. (2003). Efficient and anonymous web-usage mining for web personalization. INFORMS Journal on Computing, 15 (2), 123-147.
Eirinaki, M., & Vazirgiannis, M. (2003). Web mining for web personalization.ACM Transactions on Internet Technology (TOIT), 3(1), 1-27.
Srivastava, J., Cooley, R., Deshpande, M., & Tan, P. N. (2000). Web usage mining: Discovery and applications of usage patterns from web data. AcmSigkdd Explorations Newsletter, 1(2), 12-23.
Baraglia, R., & Silvestri, F. (2007). Dynamic personalization of web sites without user intervention. Communications of the ACM, 50(2), 63-67.
Frias-Martinez, E., & Karamcheti, V. (2003). Reduction of user perceived latency for a dynamic and personalized site using web-mining techniques. In Proceedings of WebKDD (pp. 47-57).
Pacheco-Torgal, F., Castro-Gomes, J., & Jalali, S. (2008). Alkali-activated binders: A review: Part 1. Historical background, terminology, reaction mechanisms and hydration products. Construction and Building Materials, 22(7), 1305-1314.
Fayyad, U. M. (1996). Data mining and knowledge discovery: Making sense out of data. IEEE Expert: Intelligent Systems and Their Applications, 11(5), 20-25.
Borges, J., & Levene, M. (2000). Data mining of user navigation patterns. In Web usage analysis and user profiling (pp. 92-112). Springer Berlin Heidelberg.
Mary, S. S. A., & Malarvizhi, M. (2013). Integrated Web Recommendation Model with Improved Weighted Association Rule Mining. International Journal of Data Mining & Knowledge Management Process, 3(2), 87.
Bin Ramli, A. A. (2001). Web usage mining using apriori algorithm: UUM learning care portal case. In Proc. of the Int. Conf. on Knowledge Management (pp. 1-19).
Prasetyo, B., Pramudiono, I., Takahashi, K., & Kitsuregawa, M. (2002, May). Naviz: Website navigational behavior visualizer. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 276-289). Springer Berlin Heidelberg.
Etminani, K., Akbarzadeh- Totonchi, M. R., & Yanehsari, N. R. (2009). Web Usage Mining: users' navigational patterns extraction from web logs using ant-based clustering method. In IFSA/EUSFLAT Conf. (pp. 396-401).
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.