This Article 
 Bibliographic References 
 Add to: 
Discovery Visualization Using Fast Clustering
September/October 1999 (vol. 19 no. 5)
pp. 32-39
This article describes discovery visualization, a new visual data mining approach that has as a key element the heightened awareness of the user by the machine. Discovery visualization promotes the concept of continuous interaction with constant feedback between human and machine, and constant unfolding of the data. It does this by providing a combination of automated response and user selection to achieve and sustain animated action while the user explores time-dependent data. The process begins by automatically generating an overview using a fast clustering approach, where the clusters are then followed as time-dependent features. We applied discovery visualization to both test data and real application data. The results show that the method is accurate and scalable, and it offers a straightforward, error-based process for improving accuracy.

1. V. Ganti et al., Clustering Large Data Sets in Arbitrary Metric Spaces, tech. report, Univ. of Wisconsin at Madison, Dept. of Computer Science, 1998, / mirrored/ / PMenu.html mirrored/ top-page.html
2. 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.
3. M. Ester, H. Kriegel, and X. Xu, “Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Identification,” Proc. Fourth Int'l Symp. Large Spatial Databases (SSD '95), pp. 67–82, 1995.
4. P. Lindstrom et al., "Real-Time, Continuous Level of Detail Rendering of Height Fields," Proc. Siggraph 96, ACM Press, New York, 1996, pp. 109-118.
5. P. Lindstrom et al., An Integrated Global GIS and Visual Simulation System, Report GIT-GVU-97-07, Graphics, Visualization, and Usability Center, Georgia Inst. of Tech nology, Atlanta, 1997.
6. T. Egemen, R. Beigel, and B. Schneiderman, "Design and Evaluation of Incremental Data Structures and Algorithms for Dynamic Query Interfaces," Proc. IEEE Information Visualization 97 Symp., IEEE Computer Society Press, Los Alamitos, Calif., 1997, pp. 81-86.
7. M. Gross, "Subspace Methods for the Visualization of Multidimensional Data Sets," Scientific Visualization, Rosenblum et al., eds., Academic Press, New York, 1994, pp. 172-185.
8. H. Hagen, "Visualization of Large Data Sets," Scientific Visualization, Rosenblum et al., eds., Academic Press, New York, 1994, pp. 186-198.
9. B. Heckel and N. Hamann, "Visualization of Cluster Hierarchies," Proc. of SPIE, Vol. 3298, SPIE Press, Bellingham, Wash., 1998, pp. 162-171.
10. D. Silver, "Object Oriented Visualization," IEEE Computer Graphics and Applications, vol. 15, no. 3, pp. 54-62, May 1995.
11. T. van Walsum, F.H. Post, D. Silver, and F.J. Post, Feature Extraction and Iconic Visualization IEEE Trans. Visualization and Computer Graphics, vol. 2, no. 2, pp. 111-119, June 1996.
12. S. Bryson, "Implementing Virtual Reality," Siggraph 1993 Course #43 Notes, ACM Press, New York, 1993, pp. 1.1.1-1.6.6, 16.1-16.12.
13. W. Ribarsky et al., Fast Clustering and Feature Tracking for Exploration, Report GIT-GVU-99-21, Graphics, Visualization, and Usability Center, Georgia Inst. of Tech nology, Atlanta, 1999.
14. C. Faloutsos and K.I. Lin, “Fastmap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets,” Proc. SIGMOD, Int'l Conf. Management of Data, pp. 163-174, 1995.
1. R. Monasson et al., "Determining Computational Complexity from Characteristic 'Phase Transitions'," Nature, Vol. 400, July8 1999, pp. 133-137.
2. T.H. Cormen,C.E. Leiserson, and R.L. Rivest,Introduction to Algorithms.Cambridge, Mass.: MIT Press/McGraw-Hill, 1990.

William Ribarsky, Jochen Katz, Frank Jiang, Aubrey Holland, "Discovery Visualization Using Fast Clustering," IEEE Computer Graphics and Applications, vol. 19, no. 5, pp. 32-39, Sept.-Oct. 1999, doi:10.1109/38.788796
Usage of this product signifies your acceptance of the Terms of Use.