Visually Effective Information Visualization of Large Data

Recent technology developments produce an increasingly large volume of information. Therefore visualization of these data requires sophisticated and efficient methods that take the amount of data into account. The information often gets lost or hidden in displays of traditional information visualization techniques. A significant improvement can be achieved using clustering and visual abstraction. The synergetic approach introduced here combines visual and computer data mining. Its effect is demonstrated on a popular information visualization method – the parallel coordinates.

[1]  Harri Siirtola.  Direct manipulation of parallel coordinates , 2000, CHI Extended Abstracts.

[2]  Chris Stolte,et al.  Rendering effective route maps: improving usability through generalization , 2001, SIGGRAPH.

[3]  Herman Chernoff,et al.  The Use of Faces to Represent Points in k- Dimensional Space Graphically , 1973 .

[4]  J.A. Corral,et al.  Image compression via optimal vector quantization: a comparison between SOM, LBG and k-means algorithms , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[5]  Edward R. Tufte,et al.  Envisioning Information , 1990 .

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

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

[8]  Steven K. Feiner,et al.  Worlds within worlds: metaphors for exploring n-dimensional virtual worlds , 1990, UIST '90.

[9]  Markus Hadwiger,et al.  High-quality two-level volume rendering of segmented data sets on consumer graphics hardware , 2003, IEEE Visualization, 2003. VIS 2003..

[10]  Alfred Inselberg,et al.  Parallel coordinates for visualizing multi-dimensional geometry , 1987 .

[11]  Matthew O. Ward,et al.  Exploring N-dimensional databases , 1990, Proceedings of the First IEEE Conference on Visualization: Visualization `90.

[12]  Alejandro Murua,et al.  Hierarchical model-based clustering of large datasets through fractionation and refractionation , 2004, Inf. Syst..

[13]  Richard A. Becker,et al.  Brushing scatterplots , 1987 .

[14]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[15]  Holger Theisel.  Higher Order Parallel Coordinates , 2000, VMV.

[16]  M. E. McGill,et al.  Dynamic Graphics for Statistics , 1988 .

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

[18]  Helwig Hauser,et al.  An Interaction View on Information Visualization , 2003, Eurographics.

[19]  Jean-Daniel Fekete,et al.  Interactive information visualization of a million items , 2002, IEEE Symposium on Information Visualization, 2002. INFOVIS 2002..

[20]  Daniel A. Keim,et al.  Pixel bar charts: a visualization technique for very large multi-attribute data sets? , 2002, Inf. Vis..