Revealing Structure in Visualizations of Dense 2D and 3D Parallel Coordinates

Parallel coordinates is a well-known technique used for visualization of multivariate data. When the size of the data sets increases the parallel coordinates display results in an image far too cluttered to perceive any structure. We tackle this problem by constructing high-precision textures to represent the data. By using transfer functions that operate on the high-precision textures, it is possible to highlight different aspects of the entire data set or clusters of the data. Our methods are implemented in both standard 2D parallel coordinates and 3D multi-relational parallel coordinates. Furthermore, when visualizing a larger number of clusters, a technique called ‘feature animation’ may be used as guidance by presenting various cluster statistics. A case study is also performed to illustrate the analysis process when analysing large multivariate data sets using our proposed techniques.

[1]  Dorian Pyle,et al.  Data Preparation for Data Mining , 1999 .

[2]  Alfred Inselberg,et al.  The plane with parallel coordinates , 1985, The Visual Computer.

[3]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[4]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[5]  Hans Hinterberger,et al.  Data density: a powerful abstraction to manage and analyze multivariate data , 1987 .

[6]  Edward J. Wegman,et al.  High Dimensional Clustering Using Parallel Coordinates and the Grand Tour , 1997 .

[7]  Liz J. Stuart,et al.  Animator: a tool for the animation of parallel coordinates , 2004, Proceedings. Eighth International Conference on Information Visualisation, 2004. IV 2004..

[8]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Haim Levkowitz,et al.  Uncovering Clusters in Crowded Parallel Coordinates Visualizations , 2004, IEEE Symposium on Information Visualization.

[10]  Matthew D. Cooper,et al.  3-dimensional display for clustered multi-relational parallel coordinates , 2005, Ninth International Conference on Information Visualisation (IV'05).

[11]  José Fernando Rodrigues,et al.  Frequency plot and relevance plot to enhance visual data exploration , 2003, 16th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2003).

[12]  Simon Parsons,et al.  Principles of Data Mining by David J. Hand, Heikki Mannila and Padhraic Smyth, MIT Press, 546 pp., £34.50, ISBN 0-262-08290-X , 2004, The Knowledge Engineering Review.

[13]  Tobias Isenberg,et al.  An interactive 3D integration of parallel coordinates and star glyphs , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[14]  E. Wegman,et al.  Construction of line densities for parallel coordinate plots , 1992 .

[15]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[16]  Lawrence O. Hall,et al.  Visualizing fuzzy points in parallel coordinates , 2003, IEEE Trans. Fuzzy Syst..

[17]  Matthew O. Ward,et al.  Hierarchical parallel coordinates for exploration of large datasets , 1999, Proceedings Visualization '99 (Cat. No.99CB37067).

[18]  N. Andrienko,et al.  Parallel coordinates for exploring properties of subsets , 2004, Proceedings. Second International Conference on Coordinated and Multiple Views in Exploratory Visualization, 2004..

[19]  Matej Novotny,et al.  Visually Effective Information Visualization of Large Data , 2004 .

[20]  Alfred Inselberg,et al.  Parallel coordinates: a tool for visualizing multi-dimensional geometry , 1990, Proceedings of the First IEEE Conference on Visualization: Visualization `90.

[21]  M. Cooper,et al.  Revealing structure within clustered parallel coordinates displays , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..