Value and Relation Display for Interactive Exploration of High Dimensional Datasets

Traditional multidimensional visualization techniques, such as glyphs, parallel coordinates and scatterplot matrices, suffer from clutter at the display level and difficult user navigation among dimensions when visualizing high dimensional datasets. In this paper, we propose a new multidimensional visualization technique named a value and relation (VaR) display, together with a rich set of navigation and selection tools, for interactive exploration of datasets with up to hundreds of dimensions. By explicitly conveying the relationships among the dimensions of a high dimensional dataset, the VaR display helps users grasp the associations among dimensions. By using pixel-oriented techniques to present values of the data items in a condensed manner, the VaR display reveals data patterns in the dataset using as little screen space as possible. The navigation and selection tools enable users to interactively reduce clutter, navigate within the dimension space, and examine data value details within context effectively and efficiently. The VaR display scales well to datasets with large numbers of data items by employing sampling and texture mapping. A case study on a real dataset, as well as the VaR displays of multiple real datasets throughout the paper, reveals how our proposed approach helps users interactively explore high dimensional datasets with large numbers of data items

[1]  Matthew O. Ward,et al.  XmdvTool: integrating multiple methods for visualizing multivariate data , 1994, Proceedings Visualization '94.

[2]  Matthew O. Ward,et al.  Visual Hierarchical Dimension Reduction for Exploration of High Dimensional Datasets , 2003, VisSym.

[3]  Daniel A. Keim,et al.  The Gridfit algorithm: an efficient and effective approach to visualizing large amo , 1998 .

[4]  Dimitrios Gunopulos,et al.  Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.

[5]  Hans-Peter Kriegel,et al.  VisDB: database exploration using multidimensional visualization , 1994, IEEE Computer Graphics and Applications.

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

[7]  D. F. Andrews,et al.  PLOTS OF HIGH-DIMENSIONAL DATA , 1972 .

[8]  Rajeev Motwani,et al.  Random sampling for histogram construction: how much is enough? , 1998, SIGMOD '98.

[9]  Jonathan Goldstein,et al.  When Is ''Nearest Neighbor'' Meaningful? , 1999, ICDT.

[10]  Stefan Berchtold,et al.  Similarity clustering of dimensions for an enhanced visualization of multidimensional data , 1998, Proceedings IEEE Symposium on Information Visualization (Cat. No.98TB100258).

[11]  Pak Chung Wong,et al.  Dynamic visualization of transient data streams , 2003, IEEE Symposium on Information Visualization 2003 (IEEE Cat. No.03TH8714).

[12]  Daniel A. Keim,et al.  Designing Pixel-Oriented Visualization Techniques: Theory and Applications , 2000, IEEE Trans. Vis. Comput. Graph..

[13]  James J. Thomas,et al.  Visualizing the non-visual: spatial analysis and interaction with information from text documents , 1995, Proceedings of Visualization 1995 Conference.

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

[15]  Michael Stonebraker,et al.  Constant density visualizations of non-uniform distributions of data , 1998, UIST '98.

[16]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[17]  Matthew O. Ward,et al.  A Taxonomy of Glyph Placement Strategies for Multidimensional Data Visualization , 2002, Inf. Vis..

[18]  Matthew O. Ward,et al.  Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets , 2003, IEEE Symposium on Information Visualization 2003 (IEEE Cat. No.03TH8714).

[19]  Peter Z. Kunszt,et al.  The SDSS skyserver: public access to the sloan digital sky server data , 2001, SIGMOD '02.

[20]  I. Jolliffe.  Principal Component Analysis , 2002 .

[21]  Chris Buckley,et al.  OHSUMED: an interactive retrieval evaluation and new large test collection for research , 1994, SIGIR '94.

[22]  Hans-Peter Kriegel,et al.  Recursive pattern: a technique for visualizing very large amounts of data , 1995, Proceedings Visualization '95.