EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 125 (2017) MATEC Web Conf., 125 (2017) 04025 References
Open Access
Issue
MATEC Web Conf.
Volume 125, 2017
21st International Conference on Circuits, Systems, Communications and Computers (CSCC 2017)
Article Number 04025
Number of page(s) 8
Section Computers
DOI https://doi.org/10.1051/matecconf/201712504025
Published online 04 October 2017
  1. Farney, T. A. Click analytics: Visualizing website use data. Information Technology and Libraries, 30(3), 141, (2011) [CrossRef] [Google Scholar]
  2. Kimball, R., & Merz, R. The data webhousetoolkit. Wiley (2000). [Google Scholar]
  3. Phippen, A., Sheppard, L., & Furnell, S. (2004). Apractical evaluation of Web analytics. Internet Research, 14(4), 284–293. [CrossRef] [Google Scholar]
  4. Gonçalves, B., & Ramasco, J. J. (2008). Human dynamics revealed through Web analytics. Physical Review E, 78(2), 026123. [CrossRef] [Google Scholar]
  5. Plaza, B. Monitoring web traffic source effectiveness with Google Analytics: An experiment with time series. In Aslib Proceedings (Vol. 61, No. 5, pp. 474–482), (2009).Emerald Group Publishing Limited. [CrossRef] [Google Scholar]
  6. Kohavi, R., Rothleder, N. J., & Simoudis, E.Emerging trends in business analytics. Communications of the ACM, 45(8), 45–48, (2002). [CrossRef] [Google Scholar]
  7. Hasan, L., Morris, A., & Probets, S. Using GoogleAnalytics to evaluate the usability of e-commercesites. Human centered design, 697–706, (2009). [CrossRef] [Google Scholar]
  8. Kohavi, R., Mason, L., Parekh, R., & Zheng, Z.Lessons and challenges from mining retail e-commerce data. Machine Learning, 57(1), 83–113, (2004). [CrossRef] [Google Scholar]
  9. White, T. Hadoop: The definitive guide. “O’ReillyMedia, Inc.”, (2012). [Google Scholar]
  10. Flanagan, D. JavaScript: the definitive guide. “O’Reilly Media, Inc.”, (2006). [Google Scholar]
  11. Bucklin, R. E., & Sismeiro, C. Click here for Internet insight: Advances in clickstream data analysis in marketing. Journal of Interactive Marketing, 23(1), 35–48, (2009). [Google Scholar]
  12. Montgomery, A. L., Li, S., Srinivasan, K., & Liechty, J. C. Modeling online browsing and path analysis using clickstream data. Marketing science, 23(4), 579–595, (2004). [CrossRef] [Google Scholar]
  13. Moe, W. W., & Fader, P. S. Capturing evolving visit behavior in clickstream data. Journal of Interactive Marketing, 18(1), 5–19, (2004). [CrossRef] [Google Scholar]
  14. Van den Poel, D., & Buckinx, W. Predicting online-purchasing behaviour. European journal of operational research, 166(2), 557–575, (2005). [CrossRef] [Google Scholar]
  15. Danaher, P. J., Mullarkey, G. W., & Essegaier, S. Factors affecting web site visit duration: a cross-domain analysis. Journal of Marketing Research, 43(2), 182–194, (2006). [CrossRef] [Google Scholar]
  16. Kateja, R., Rohith, A., Kumar, P., & Sinha, R. VizClick visualizing clickstream data. In Information Visualization Theory and Applications(IVAPP), 2014 International Conference on (pp. 247–255). IEEE, (2014). [Google Scholar]
  17. De Oliveira, M. F., & Levkowitz, H. From visual data exploration to visual data mining: a survey. IEEE Transactions on Visualization and Computer Graphics, 9(3), 378–394, (2003). [CrossRef] [Google Scholar]
  18. Moe, W. W. An empirical two-stage choice model with varying decision rules applied to internet clickstream data. Journal of Marketing Research, 43(4), 680–692, (2006). [CrossRef] [Google Scholar]
  19. De Bock, K., & Van den Poel, D. Predictingwebsite audience demographics for web advertising targeting using multi-website clickstream data. Fundamenta Informaticae, 98(1),49–70, (2010). [EDP Sciences] [Google Scholar]
  20. Chen, L., & Su, Q. Discovering user’s interest at E-commerce site using clickstream data. In Service systems and service management(ICSSSM), 2013 10th international conference on (pp. 124–129). IEEE, (2013). [Google Scholar]
  21. Schellong, D., Kemper, J., & Brettel, M. Clickstream data as a source to uncover consumer shopping types in a large-scale online setting, (2016). [Google Scholar]
  22. Shi, C., Fu, S., Chen, Q., & Qu, H. VisMOOC: Visualizing video clickstream data from massive open online courses. In Visualization Symposium(PacificVis), 2015 IEEE Pacific (pp. 159–166).IEEE, (2015). [Google Scholar]
  23. Brinton, C. G., & Chiang, M. Mooc performance prediction via clickstream data and social learning networks. In Computer Communications(INFOCOM), 2015 IEEE Conference on (pp.2299–2307). IEEE, (2015). [Google Scholar]
  24. Gilks, W. R., Richardson, S., & Spiegelhalter, D.(Eds.), Markov chain Monte Carlo in practice. CRC press, (1995). [Google Scholar]
  25. http://www.msnbc.com [Google Scholar]
  26. Steinwart, I., & Christmann, A. Support vectormachines. Springer Science & Business Media, (2008). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.