VizClick - Visualizing Clickstream Data

Rajat Kateja, Amerineni Rohith, Piyush Kumar, Ritwik Sinha

2014

Abstract

Clickstream data is ubiquitous in today’s web-connected world. Such data displays the salient features of big data, that is, volume, velocity and variety. As with any big data, visualizations can play a central role in making sense and generating hypotheses from such data. In this paper, we present a systematic approach of visualizing clickstream data. There are three basic questions we aim to address. First, we explore the interdependence between the large number of dimensions that are measured in clickstream data. Next, we analyze spatial aspects of data collected in web-analytics. Finally, the web designers might be interested in getting a deeper understanding of the website’s topography and how browsers are interacting with it. Our approach is designed for business analysts, web designers and marketers; and helps them draw actionable insights in the management and refinement of large websites.

References

  1. Agresti, A. (2002). Categorical data analysis, volume 359. John Wiley & Sons.
  2. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008.
  3. Bostock, M., Ogievetsky, V., and Heer, J. (2011). D3 data-driven documents. Visualization and Computer Graphics, IEEE Transactions on, 17(12):2301-2309.
  4. Brainerd, J. and Becker, B. G. (2001). Case study: Ecommerce clickstream visualization. In infovis, pages 153-156.
  5. Cleveland, W. S. (1993). Visualizing data. Hobart Press.
  6. Dougenik, J. A., Chrisman, N. R., and Niemeyer, D. R. (1985). An algorithm to construct continuous area cartograms. The Professional Geographer, 37(1):75-81.
  7. Ferreira de Oliveira, M. C. and Levkowitz, H. (2003). From visual data exploration to visual data mining: A survey. Visualization and Computer Graphics, IEEE Transactions on, 9(3):378-394.
  8. Freedman, D. and Diaconis, P. (1981). On the histogram as a density estimator: L-2 theory. Probability theory and related fields, 57(4):453-476.
  9. Kutner, M. H., Nachtsheim, C., Neter, J., et al. (2004). Applied linear regression models. McGraw-Hill New York.
  10. Lee, J., Podlaseck, M., Schonberg, E., and Hoch, R. (2001). Visualization and analysis of clickstream data of online stores for understanding web merchandising. In Applications of Data Mining to Electronic Commerce, pages 59-84. Springer.
  11. McAfee, A., Brynjolfsson, E., et al. (2012). Big data: the management revolution. Harvard business review, 90(10):60-66.
  12. Press, W. H. (1992). Numerical recipes in Fortran 77: the art of scientific computing, volume 1. Cambridge university press.
  13. R Core Team (2013). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
  14. Sarkar, D. (2008). Lattice: multivariate data visualization with R. Springer.
  15. Sturges, H. A. (1926). The choice of a class interval. Journal of the American Statistical Association, 21(153):65-66.
  16. Swayne, D. F., Lang, D. T., Buja, A., and Cook, D. (2003). Ggobi: Evolving from xgobi into an extensible framework for interactive data visualization. Computational Statistics & Data Analysis, 43(4):423-444.
  17. Tufte, E. R. and Graves-Morris, P. (1983). The visual display of quantitative information, volume 2. Graphics press Cheshire, CT.
  18. Wei, J., Shen, Z., Sundaresan, N., and Ma, K.-L. (2012). Visual cluster exploration of web clickstream data. In Visual Analytics Science and Technology (VAST), 2012 IEEE Conference on, pages 3-12. IEEE.
  19. Wickham, H. (2009). Ggplot2: elegant graphics for data analysis. Springer Publishing Company, Incorporated.
Download


Paper Citation


in Harvard Style

Kateja R., Rohith A., Kumar P. and Sinha R. (2014). VizClick - Visualizing Clickstream Data . In Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014) ISBN 978-989-758-005-5, pages 247-255. DOI: 10.5220/0004687102470255


in Bibtex Style

@conference{ivapp14,
author={Rajat Kateja and Amerineni Rohith and Piyush Kumar and Ritwik Sinha},
title={VizClick - Visualizing Clickstream Data},
booktitle={Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014)},
year={2014},
pages={247-255},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004687102470255},
isbn={978-989-758-005-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014)
TI - VizClick - Visualizing Clickstream Data
SN - 978-989-758-005-5
AU - Kateja R.
AU - Rohith A.
AU - Kumar P.
AU - Sinha R.
PY - 2014
SP - 247
EP - 255
DO - 10.5220/0004687102470255