Analysis of Web Visit Histories, Part II: Predicting Navigation by Nested STUMP Regression Trees
- 56 Downloads
This paper constitutes part II of the contribution to the analysis of web visit histories through a new methodological framework for web usage-structure mining considering association rules theory. The aim is to explore through a tree structure the sequence of direct rules (i.e. paths) that characterize a web navigator who keeps standing longer on a web page with respect to the path characterizing navigators who leave the web earlier. A novel tree-based structure is introduced to take into account that the learning sample changes click by click leaving out navigators who drop off from the web after any click. The response variable at each time point is the remaining number of clicks before leaving the web. The split is induced by the predictors that describe the preferred web sections. The methodology introduced results in a Nested Stump Regression Tree that is an hierarchy of stump trees, where a stump is a tree with only one split or, equivalently, with only two terminal nodes. Suitable properties are outlined. As in first part of the contribution to the analysis of the web visit histories, a methodological description is provided by considering a web portal with a fixed set of web sections, i.e. a data set coming from the UCI Machine Learning Repository.
KeywordsWeb path Sequence rules Recursive partitioning Web Usage-Structure Mining
Unable to display preview. Download preview PDF.
- AGRAWAL, R., and SRIKANT, R. (1994), “Fast Algorithms for Mining Association Rules”, Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Vol. 1215, pp. 487–499.Google Scholar
- BLANC, E., and GIUDICI, P. (2002), “Sequence Rules for Web Clickstream Analysis”, in Advances in Data Mining, Berlin, Heidelberg: Springer, pp. 1–14.Google Scholar
- CHAKRABARTI, S. (2002), Mining the Web: Discovering Knowledge from Hypertext Data, The Netherlands: Elsevier.Google Scholar
- D’AMBROSIO, A., PECORARO, M., and SICILIANO, R. (2008), “Web Preferences Visualization Through Multidimensional Scaling and Trees”, in DATAVIZ VI International Conference: Statistical Graphics: Data and Information Visualization in Today’s Multimedia Society, Bremen, June 25–28, 2008.Google Scholar
- DIETTERICH, T.G. (2000), “Ensemble Methods in Machine Learning”, in Multiple Classifier Systems, Berlin: Springer, pp. 1–15.Google Scholar
- FOKKEMA, M., SMITS, N., ZEILEIS, A., HOTHORN, T., and KELDERMAN, H. (2015), “Detecting Treatment-Subgroup Interactions in Clustered Data with Generalized Linear Mixed-Effects Model Trees”, Working Papers, Faculty of Economics and Statistics, University of Innsbruck, ftp://ftp.repec.org/opt/ReDIF/RePEc/inn/wpaper/2015-10.pdf.
- IBA, W., and LANGLEY, P. (1992), “Induction of One-Level Decision Trees”, in Proceedings of the Ninth International Conference on Machine Learning, pp. 233–240.Google Scholar
- KOSALA, R., and BLOCKEEL, H. (2000), “Web Mining Research: A Survey”, ACM SIGKDD Explorations, 2, 1–15.Google Scholar
- LINOFF, G.S, and BERRY, M.J. (2001), Mining the Web: Transforming Customer Data into Customer Value, New York: John Wiley and Sons, Inc.Google Scholar
- PECORARO, M., and SICILIANO, R. (2008), “Statistical Methods for User Profiling in Web Usage Mining”, in Handbook of Research on Text and Web Mining Technologies, eds. M. Song and Y.B. Wu, Hershey PA: Idea Group Inc., pp. 359–368.Google Scholar
- SICILIANO, R., and MOLA, F. (1996), “A Fast Regression Tree Procedure”, in Proceedings of the 11th International Workshop on Statistical Modeling, eds. A. Forcina, G.M. Marchetti, R. Hatzinger, and G. Galmacci, Citta’ di Castello IT: Graphos, pp. 332–340.Google Scholar
- VEZZOLI, M. (2011), “Exploring the Facets of Overall Job Satisfaction Through a Novel Ensemble Learning”, Electronic Journal of Applied Statistical Analysis, 4(1), 23–38.Google Scholar