Mapping Multi-dimensional Data – Integrating Mining and Visualization

• Fernando V. Paulovich USP
• Rosane Minghim USP

Abstract

The projection or point placement techniques, which seek to map multi-dimensional data onto visual spaces, have been the interest of the visual data analysis community for a long time due to their ability for exploratory tasks based on similarity and correlation. However, many problems still persist, impairing their application. The main contribution of this paper is a further understanding of the problems found on the current techniques and the development of projection techniques which are fast, appropriately define groups of highly similar data instances, separate these groups on the final layout, and allow the data exploration on different levels of detail. In addition, we integrate some data mining features to the process of multidimensional visualization, mainly on the application of projections to the visualization of document collections.

References

Cuadros, A. M., Paulovich, F. V., Minghim, R., and Telles, G. P. (2007). Point placement by phylogenetic trees and its application for visual analysis of document collections. In Proc. of IEEE VAST 2007, pages 99–106.

Eick, S. G. and Karr, A. F. (2002). Visual scalability. Journal of Computational & Graphical Statistics, 11(1):22–43.

Lopes, A. A., Pinho, R., Paulovich, F. V., and Minghim, R. (2007). Visual text mining using association rules. Computer & Graphics, 31(3):316–326.

Merino, M. M. and Muñoz, A. (2004). A new sammon algorithm for sparse data visualization. In Proceedings of ICPR’04, pages 477–481, Washington, USA. IEEE CS.

Paulovich, F. V. (2008). Mapeamento de Dados Multi-Dimensionais – Integrando Mineração e Visualização. Tese Doutorado, ICMC/USP.

Paulovich, F. V. and Minghim, R. (2006). Text map explorer: a tool to create and explore document maps. In Proc. of IV’06, pages 245–251, Washington, USA. IEEE CS.

Paulovich, F. V. and Minghim, R. (2008). HiPP: A novel hierarchical point placement strategy and its application to the exploration of document collections. IEEE Trans. on Vis. and Comp. Graph. (Proc. of InfoVis 2008), 14(6):1229–1236.

Paulovich, F. V., Nonato, L. G., Minghim, R., and Levkowitz, H. (2008a). Least square projection: a fast high precision multidimensional projection technique and its application to document mapping. IEEE Trans. on Vis. and Comp. Graph., 14(3):564–575.

Paulovich, F. V., Oliveira, M. C. F., and Minghim, R. (2007). The projection explorer: A flexible tool for projection-based multidimensional visualization. In Proc. of SIBGRAPI 2007, pages 27–36, Washington, USA. IEEE CS.

Paulovich, F. V., Pinho, R., Botha, C. P., Heijs, A., and Minghim, R. (2008b). Pex-web: Content-based visualization of web search results. In Proc. of IV’08, pages 208–214, Los Alamitos, USA. IEEE CS.

Roweis, S. T. and Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323–2326.

Salton, G. (1991). Developments in automatic text retrieval. Science, 253:974–979.

Sorkine, O. and Cohen-Or, D. (2004). Least-squares meshes. In Proc. of SMI’04, pages 191–199, Washington, USA. IEEE CS.

Tan, P.-N., Steinbach, M., and Kumar, V. (2005). Introduction to Data Mining, (First Edition). Addison-Wesley Longman Publishing Co., Inc., Boston, USA.

Tejada, E., Minghim, R., and Nonato, L. G. (2003). On improved projection techniques to support visual exploration of multidimensional data sets. Inf. Vis., 2(4):218–231.

Telles, G. P., Minghim, R., and Paulovich, F. V. (2007). Normalized compression distance for visual analysis of document collections. Computer & Graphics, 31(3):327–337.

Thomas, J. J. and Cook, K. A., editors (2005). Illuminating the path: The Research and Development Agenda for Visual Analytics. IEEE CS, Los Alamitos, USA.

Wong, P. C. (1999). Visual data mining. IEEE Comp. Graph. and App., 19(5):20–21.