Eser Kandogan
ABSTRACT
Interactive visualizations are effective tools in mining scientific, engineering, and business data to support decision-making activities. Star Coordinates is proposed as a new multi-dimensional visualization technique, which supports various interactions to stimulate visual thinking in early stages of knowledge discovery process. In Star Coordinates, coordinate axes are arranged on a two-dimensional surface, where each axis shares the same origin point. Each multi-dimensional data element is represented by a point, where each attribute of the data contributes to its location through uniform encoding. Interaction features of Star Coordinates provide users the ability to apply various transformations dynamically, integrate and separate dimensions, analyze correlations of multiple dimensions, view clusters, trends, and outliers in the distribution of data, and query points based on data ranges. Our experience with Star Coordinates shows that it is particularly useful for the discovery of hierarchical clusters, and analysis of multiple factors providing insight in various real datasets including telecommunications churn.
- 1.Ankerst, M., Keim, D. A., Kriegel, H.-P., Circle Segments: A Technique for Visually Exploring Large Multidimensional Data Sets. Proc. IEEE Visualization '96, Hot Topics, 1996.]]Google Scholar
- 2.Bertin, J., Graphics and Graphic Information Processing, Walter de Gruyer & Co., Berlin, 24-31, 1981.]]Google Scholar
- 3.Buja, A., Swayne, D. F., Littman, M., Dean, N., XGvis: Interactive Data Visualization with Multidimensional Scaling, to appear in Journal of Computational and Graphical Statistics.]]Google Scholar
- 4.Chemoff, H., The Use of Faces to Represent Points in k-Dimensional Space Graphically, Journal of American Statistical Association, 68, 361-368.]]Google Scholar
- 5.Derthiek, M., Kolojejchiek, J., Roth, S. F., An interactive visualization environment for data exploration. Proc. of ACM SIGKDD '97, pp. 2-9, 1997.]]Google Scholar
- 6.Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM 39, 11.]] Google ScholarDigital Library
- 7.Feiner, S., Beshers, C., Worlds within Worlds: Metaphors for Exploring n-Dimensional Virtual Worlds. Proc. UIST '90, pp. 76-83, 1990.]] Google ScholarDigital Library
- 8.Feldman, R., Kloesgen, W., Zilberstein, A., Visualization Techniques to Explore Data Mining Results for Document Collections. Proc of ACM SIGKDD '97, pp. 16-23, 1997.]]Google Scholar
- 9.Fienberg, S. E., Graphical methods in statistics., American Statisticians, 33, 165-178, 1979.]]Google Scholar
- 10.Grinstein, G. G., Harnessing the Human in Knowledge Discovery, Proc. of ACM SIGKDD '96, pp. 384-385, 1996.]]Google Scholar
- 11.Johnson, R.R., Visualization of Multi-Dimensional Data with Vector-fusion. Proe of IEEE Visualization '00, pp. 297-302, 2000.]] Google ScholarDigital Library
- 12.Inselberg, A., Parallel Coordinates: A guide for the Perplexed, Proc. of IEEE Conference on Visualization, Hot Topics, pp. 35-38, 1996.]]Google Scholar
- 13.Inselberg, A., Multidimensional Detective. Proc. of IEEE Information Visualization '97, pp. 100-107, 1997.]] Google ScholarDigital Library
- 14.Kandogan, E., Star Coordinates: A Multi-dimensional Visualization Technique with Uniform Treatment of Dimensions. Proc. of IEEE Information Visualization, Hot Topics, pp. 4-8, 2000.]]Google Scholar
- 15.Keim, D. A., Kriegel, H.-P., VisDB: Database Explorations Using Multidimensional Visualization. IEEE Computer Graphics and Applications, 40-49, 1994.]] Google ScholarDigital Library
- 16.Kohonen, T., Self-organized formation of topologically correct feature maps. Biological Cybernetics 43, 59-- 69.]]Google ScholarCross Ref
- 17.Lagus, K., Honkela, T., Kaski, S., Kohonen, T., Self- Organizing Maps of Document Collections: A New Approach to Interactive Exploration. Proc. of ACM SIGKDD '96, pp. 238-243, 1996.]]Google Scholar
- 18.Lee H-Y., Ong, H-L, Quek, L-H., Exploiting Visualization in Knowledge Discovery. Proe. of SIGKDD, pp. 198-203, 1995.]]Google Scholar
- 19.Mihalisin, T., Timlin, J., Sehwegler, J., Visualizing Multivariate Functions, Data, and Distributions. IEEE Computer Graphics and Applications, 11(13), 28-35, 1991.]] Google ScholarDigital Library
- 20.Mihalisin, T., Timlin, J., Schwegler, J., Visualization and Analysis of Multi-Variate Data: A Technique for All Fields. Proc. of IEEE Visualization '91, pp. 171- 178, 1991.]] Google ScholarDigital Library
- 21.Rao, R., and Card, S. K., The Table Lens: Merging Graphical and Symbolic Representations in an Interactive Focus + Context Visualization for Tabular Information. Proc. CHI '94, pp. 318-322, 1994.]] Google ScholarDigital Library
- 22.Shneiderman, B., Dynamic Queries for Visual Information Seeking. IEEE Software, 11(6), 70-77, 1994.]] Google ScholarDigital Library
- 23.Spoerri, A., InfoCrystal: A Visual Tool for Information Retrieval. Proc. of IEEE Visualization '93, pp. 150- 157, 1993.]] Google ScholarDigital Library
- 24.Tufie, E. R., The Visual Display of Quantitative Information, Graphics Press, Cheshire, Connecticut, 1983.]] Google ScholarDigital Library
- 25.van Wijk, J. J., van Liere, R. D., Hyperslice. Proc. Visualization '93, pp. 119-125, 1993.]] Google ScholarDigital Library
- 26.Wright, W., Information Animation Applications in the Capital Markets. Proc. of IEEE Information Visualization '95, pp. 136-137, 1995.]] Google ScholarDigital Library
Index Terms
- Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
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