Pencarian Pola Akses Pengunjung Toko Online Menggunakan Weighted Graph Web Usage Mining
Abstract
The growth of online stores is proportional to theincrease in web usage data that is generated. Web Usage Mining can generate useful information based on web usage data. This information is required by the owner of the online shop to find information on frequently accessed pages and demanded items by visitors. This study is using Weighted Graph Web Usage Mining method for generating online shoppers access patterns. This methode include collecting web usage data when client using AJAX interface in real time, ¬ pre-processing to generate the database traversal and discovering pattern with Weighted Frequent Patterns Mining methods. The results show that Weighted Graph Web Usage Mining can generate informations about frequently accessed pages and demanded items by visitors in a given period based on visitors access pattern.
References
B. Liu, Web Data Mining, Second. Berlin: Springer Verlag New York, Inc., 2011, p. 605.
Q. Song and M. Shepperd, “Mining web browsing patterns for Ecommerce,” Computers in Industry, vol. 57, no. 7, pp. 622–630, Sep. 2006.
G. Velayathan, “Behavior-Based Web Page Evaluation,” in IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2006, pp. 6–9.
M. Heydari, R. A. Helal, and K. I. Ghauth, “A Graph-Based Web Usage Mining Method Considering Client Side Data,” in 2009 International Conference on Electrical Engineering and Informatics, 2009, no. August, pp. 147 153.
M. Heydari, R. Alsaqour, K. Imran, and K. Vaziry, “A Weighted Graph Web Usage Mining Method to Evaluate Usage of Websites Department of Computer Science , Faculty of Information Science and Technology , University,” Australian Journal of Basic and Applied Sciences, vol. 5, no. 9, pp. 1606–1616, 2011.
U. Fayyad, G. Piatetsky-shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in,” American Association for Artificial Intelligence, vol. 17, no. 3, pp. 37–54, Jul-1996.
N. K. Tyagi, A. K. Solanki, and M. Wadhwa, “Analysis of Server Log by Web Usage Mining for Website Improvement,” IJCSI International Journal of Computer Science, vol. 7, no. 4, pp. 17–21, 2010.
S. S. Skiena, The Algorithm Design Manual, Second. London: Springer-Verlag London, 2008.
S. D. Lee and H. C. Park, “Mining Weighted Frequent Patterns from Path Traversals on Weighted Graph,” IJCSNS International Journal of Computer Science and Network Security, vol. 7, no. 4, pp. 140–148, 2007.
K. Mihara, Koichiro; Terabe, Masahiro; Hashimoto, “A Graph-Based Web Usage Mining Considering Page Browsing Time,” in KICSS 2007 : The Second International Conference on Knowledge, Information and Creativity Support Systems, 2007.
G. Li, T. Beijing, I. Engineering, B. Yang, and J. Guo, “Weighted Frequent Patterns Mining Over Data Streams,” in 2nd International Conference on Industrial and Information Systems Weighted Frequent Patterns Mining Over Data Stream, 2010, no. c, pp. 1–4.
H. Kim and P. K. Chan, “Implicit indicators for interesting web pages,” in International Conference on Web Information Systems, 2005.
M. Claypool, D. Brown, P. Le, and M. Waseda, “Inferring User Interest,” IEEE Internet Computing, vol. 5, no. 6, pp. 32–39, 2001.
© Jurnal Nasional Teknik Elektro dan Teknologi Informasi, under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License.