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Structure-Based Brushes: A Mechanism for Navigating Hierarchically Organized Data and Information Spaces
April-June 2000 (vol. 6 no. 2)
pp. 150-159

Abstract—Interactive selection is a critical component in exploratory visualization, allowing users to isolate subsets of the displayed information for highlighting, deleting, analysis, or focused investigation. Brushing, a popular method for implementing the selection process, has traditionally been performed in either screen space or data space. In this paper, we introduce an alternate, and potentially powerful, mode of selection that we term structure-based brushing, for selection in data sets with natural or imposed structure. Our initial implementation has focused on hierarchically structured data, specifically very large multivariate data sets structured via hierarchical clustering and partitioning algorithms. The structure-based brush allows users to navigate hierarchies by specifying focal extents and level-of-detail on a visual representation of the structure. Proximity-based coloring, which maps similar colors to data that are closely related within the structure, helps convey both structural relationships and anomalies. We describe the design and implementation of our structure-based brushing tool. We also validate its usefulness using two distinct hierarchical visualization techniques, namely hierarchical parallel coordinates and tree-maps. Finally, we discuss relationships between different classes of brushes and identify methods by which structure-based brushing could be extended to alternate data structures.

[1] A. Becker and S. Cleveland, “Brushing Scatterplots,” Technometrics, vol 29, no. 2, pp. 127-142, 1987.
[2] Y.-H Fua, M.O. Ward, and E.A. Rundensteiner, Hierarchical Parallel Coordinates for Exploration of Large Datasets Proc. IEEE Conf. Visualization (Vis '99), pp. 43-50, Oct. 1999.
[3] Y.H. Fua, E.A. Rundensteiner, and M.O. Ward, “Navigating Hierarchies with Structure-Based Brushes,” Proc. IEEE Symp. Information Visualization, Oct. 1999.
[4] G.W. Furnas, "Generalized Fisheye Views," Proc. CHI '86, Addison-Wesley, Reading, Mass., 1986, pp. 16-23.
[5] J. Haslett, R. Bradley, P. Craig, A. Unwin, and G. Wills, “Dynamic Graphics for Exploring Spatial Data with Application to Locating Global and Local Anomalies,” Statistical Computing, vol. 45, no. 3, pp. 234-42, 1991.
[6] A. Inselberg and B. Dimsdale, "Parallel Coordinates: A Tool for Visualizing Multi-Dimensional Geometry," Proc. Visualization '90, IEEE CS Press, 1990, pp. 361-370.
[7] C. Jeong and A. Pang, “Reconfigurable Disc Trees for Visualizing Large Hierarchical Information Space,” Proc. Information Visualization '98, pp. 19-25, 1998.
[8] B. Johnson and B. Shneiderman, “Treemaps: A Space-Filling Approach to the Visualization of Hierarchical Information,” Proc. Visualization '91 Conf., pp. 284-291, 1991.
[9] Y.K. Leung and M.D. Apperley, "A Review and Taxonomy of Distortion-Oriented Presentation Techniques," ACM Trans. on CHI, Vol. 1, No. 2, 1994, pp. 126-160.
[10] A. Martin and M. Ward, “High Dimensional Brushing for Interactive Exploration of Multivariate Data,” Proc. Visualization '95, pp. 271-278, 1995.
[11] R.J. Resnick, M.O. Ward, and E.A. Rundensteiner, “Fed—A Framework for Iterative Data Selection in Exploratory Visualization,” Proc. Int'l Conf. Scientific and Statistical Database Management '98, pp. 180-189, 1998.
[12] G.G. Robertson, J.D. Mackinlay, and S.K. Card, "Cone Trees: Animated 3D Visualizations of Hierarchical Information," Proc. ACM Conf. Human Factors in Computer Systems (CHI 91), ACM Press, 1991, pp. 189-194.
[13] D. Schaffer, Z. Zuo, S. Greenberg, L. Bartram, J. Dill, S. Dubs, and M. Roseman, “Navigating Hierarchically Clustered Networks Through Fisheye and Full-Zoom Methods,” ACM Trans. Computer–Human Interaction, vol. 3, no. 2, pp. 162–188, 1996.
[14] B. Shneiderman, “Tree Visualization with Treemaps: A 2D Space-Filling Approach,” ACM Trans. Graphics, vol. 11, no. 1, pp. 92-99, 1992.
[15] D.I. Stroe, E.A. Rundensteiner, and M.O. Ward, “Minmax Trees: Efficient Relational Operation Support for Hierarchical Data Exploration,” Technical Report WPI-CS-TR-99-37, Worcester Polytechnical Inst., Worcester, Mass., 1999.
[16] M.O. Ward, "XmdvTool: Integrating Multiple Methods for Visualizing Multivariate Data," Proc. Visualization '94, IEEE CS Press, 1994, pp. 326-336.
[17] E. Wegman, “Hyperdimensional Data Analysis Using Parallel Coordinates,” J. Am. Statistical Assoc., vol. 411, no. 85, p. 664, 1990.
[18] G.J. Wills, “Selection: 524,288 Ways to Say 'This is interesting,'” Information Visualization '96 Proc., pp. 54-60, Oct. 1996.
[19] P. Wong and R. Bergeron, “Multiresolution Multidimensional Wavelet Brushing,” Proc. Visualization '96, pp. 141-148, 1996.
[20] T. Zhang, R. Ramakrishnan, and M. Livny, "Birch: An Efficient Data Clustering Method for Very Large Databases," Proc. ACM SIGMOD Int'l Conf. Management of Data, ACM Press, 1996, pp. 103-114.

Index Terms:
Brushing, hierarchical representation, interactive selection, exploratory data analysis.
Citation:
Ying-Huey Fua, Matthew O. Ward, Elke A. Rundensteiner, "Structure-Based Brushes: A Mechanism for Navigating Hierarchically Organized Data and Information Spaces," IEEE Transactions on Visualization and Computer Graphics, vol. 6, no. 2, pp. 150-159, April-June 2000, doi:10.1109/2945.856996
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