Abstract
Clickstream can be a rich source of data for analysing user behaviour, but the volume of these logs makes it difficult to identify and categorise behavioural patterns. In this paper, we introduce the Automatic Pattern Discovery (APD) method, a technique for automated processing of Clickstream data to identify a user’s browsing patterns. The paper also includes case study that is used to illustrate the use of the APD and to evaluate its performance.
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References
Canter, D., Rivers, R., Storrs, G.: Characterising users navigation through complex data structures. Behaviour and Information Technology 4(2), 93–102 (1985)
Chi, E.H.: Improving web usability through visualisation IEEE Internet Computing, March-April 2002, pp. 64–71 (2002)
Clark, L., Ting, I., Kimble, C., Wright, P., Kudenko, D.: Combining Ethnographic and Clickstream Data to Identify Browsing Strategies Information Research 11(2), paper 249 (2006), Available at http://InformationR.net/ir/11-2/paper249.html
Cooley, R., Mobasher, B., Srivastava, J.: Data Preparation for Mining World Wide Web Browsing Patterns. Journal of Knowledge and Information System 1(1), 5–32 (1999)
Cooley, R., Tan, P.N., Srivastava, J.: Discovery of Interesting Usage Patterns from Web Data, LNCS Vol. In: Masand, B., Spiliopoulou, M. (eds.) Web Usage Analysis and User Profiling. LNCS (LNAI), vol. 1836, pp. 163–182. Springer, Heidelberg (2000)
Eick, S.G.: Visual Analysis of Website Browsing Patterns. In: Borner, K., Chen, C. (eds.) Visual Interface to Digital Libraries, pp. 65–77 (2002)
Ezeife, C.I., Lu, Y.: Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree, Data Mining and Knowledge Discovery, 10, 5–38 (2005)
Ting, I.H., Kimble, C., Kudenko, D.: Visualising and Classifying the Pattern of User’s Browsing Behaviour for Website Design Recommendation. In: Paper presented at the International Workshop on Knowledge Discovery in Data Stream, Pisa, Italy, vol. 24, pp. 101–102 (September 2004)
Ting, I.H., Kimble, C., Kudenko, D.: A Pattern Restore Method for Restoring Missing Patterns in Server Side Clickstream Data. In: Zhang, Y., Tanaka, K., Yu, J.X., Wang, S., Li, M. (eds.) APWeb 2005. LNCS, vol. 3399, pp. 501–512. Springer, Heidelberg (2005)
Xing, D., Shen, J.: Efficient Data Mining for Web Navigation Patterns. Information and Software Technology 46(1), 55–63 (2004)
Yen, S.J., Lee, Y.S.: An Efficient Data Mining Algorithm for Discovering Web Access Patterns, In Zhou, X. In: Zhou, X., Zhang, Y., Orlowska, M.E. (eds.) APWeb 2003. LNCS, vol. 2642, pp. 187–192. Springer, Heidelberg (2003)
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Ting, IH., Clark, L., Kimble, C., Kudenko, D., Wright, P. (2007). APD-A Tool for Identifying Behavioural Patterns Automatically from Clickstream Data. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74827-4_9
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DOI: https://doi.org/10.1007/978-3-540-74827-4_9
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