Visualization Support to Interactive Cluster Analysis

  • Gennady AndrienkoEmail author
  • Natalia Andrienko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9286)


We demonstrate interactive visual embedding of partition-based clustering of multidimensional data using methods from the open-source machine learning library Weka. According to the visual analytics paradigm, knowledge is gradually built and refined by a human analyst through iterative application of clustering with different parameter settings and to different data subsets. To show clustering results to the analyst, cluster membership is typically represented by color coding. Our tools support the color consistency between different steps of the process. We shall demonstrate two-way clustering of spatial time series, in which clustering will be applied to places and to time steps.


Cluster Membership Iterative Application Color Plane Call Count Color Consistency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Andrienko, G., Andrienko, N., Bak, P., Keim, D., Wrobel, S.: Visual Analytics of Movement. Springer (2013)Google Scholar
  2. 2.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann (2011)Google Scholar
  3. 3.
    Andrienko, G., Andrienko, N.: Blending aggregation and selection: Adapting parallel coordinates for the visualization of large datasets. The Cartographic Journal 42(1), 49–60 (2005)CrossRefGoogle Scholar
  4. 4.
    van Wijk, J.J., van Selow, E.R.: Cluster and calendar based visualization of time series data. In: Proc. Information Visualization, pp. 4–9 (1999)Google Scholar
  5. 5.
    Andrienko, G., Andrienko, N., Bremm, S., Schreck, T., von Landesberger, T., Bak, P., Keim, D.: Space-in-Time and Time-in-Space Self-Organizing Maps for Exploring Spatiotemporal Patterns. Computer Graphics Forum 29(3), 913–922 (2010)CrossRefGoogle Scholar
  6. 6.
    Seo, J., Shneiderman, B.: Interactively exploring hierarchical clustering results. Computer 35(7), 80–86 (2002)CrossRefGoogle Scholar
  7. 7.
    Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Transactions on Computers 18, 401–409 (1969)CrossRefGoogle Scholar
  8. 8.
    Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., Andrienko, G.: Visually driven analysis of movement data by progressive clustering. Information Visualization 7(3–4), 225–239 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Fraunhofer Institute IAISSankt AugustinGermany
  2. 2.City University LondonLondonUK

Personalised recommendations