Developing Transferable Clickstream Analytic Models Using Sequential Pattern Evaluation Indices
In this paper, a method for constructing transferable “web” and “clickstream” prediction models based on sequential pattern evaluation indices is proposed. To predict end points, click streams are assumed as sequential data. Further, a sequential pattern generation method is applied to extract features of each click stream data. Based on these features, a classification learning algorithm is applied to construct click stream end point prediction models. In this study, the evaluation indices for sequential patterns are introduced to abstract each clickstream data for transferring the constructed predictive models between different periods. This method is applied to a benchmark clickstream dataset to predict the end points. The results show that the method can obtain more accurate predictive models with a decision tree learner and a classification rule learner. Subsequently, the evaluation of the availability for transferring the predictive morels between different periods is discussed.
KeywordsSequential Pattern Mining Clickstream Analysis Transfer Learning
Unable to display preview. Download preview PDF.
- 1.Entree chicago recommendation data, http://kdd.ics.uci.edu/databases/entree/entree.html
- 2.Agrawal, R., Srikant, R.: Mining sequential patterns. In: Yu, P.S., Chen, A.L.P. (eds.) ICDE, pp. 3–14. IEEE Computer Society (1995)Google Scholar
- 4.Hettich, S., Bay, S.D.: The uci kdd archive, http://kdd.ics.uci.edu/
- 6.Nakagawa, H.: Automatic term recognition based on statistics of compound nouns. Terminology 6(2), 195–210 (2000)Google Scholar
- 7.Padmanabhan, B.: Web Clickstream Data and Pattern Discovery, vol. 3, pp. 99–116 (2009)Google Scholar
- 9.Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: Proc. of the 17th International Conference on Data Engineering, pp. 215–224. IEEE Computer Society, Los Alamitos (2001)Google Scholar
- 10.Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 185–208. MIT Press (1999)Google Scholar
- 11.Quinlan, J.R.: Programs for Machine Learning. Morgan Kaufmann Publishers (1993)Google Scholar
- 12.Shannon, C.E.: A mathematical theory of communication. The Bell System Technical Journal 27, 379–423, 623–656 (1948)Google Scholar
- 13.Sparck Jones, K.: A statistical interpretation of term specificity and its application in retrieval. Document Retrieval Systems, 132–142 (1988)Google Scholar
- 14.Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 32–41 (2002)Google Scholar
- 15.Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann (2000)Google Scholar