Techniques for precision-based visual analysis of projected data

Information Visualization

Volumn 9, Issue 3, 2010, Pages 181-193

  Schreck, Tobias ^a   Von Landesberger, Tatiana ^a,b   Bremm, Sebastian ^a  

^a DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

^b FRAUNHOFER IGD   (Germany)

Author keywords

high dimensional data analysis; point cloud visualization; projection precision

Indexed keywords

DATA CHARACTERISTICS; DATA PROPERTIES; ERROR VALUES; HIGH DIMENSIONAL DATA; INTERACTIVE SYSTEM; LEVELS OF ABSTRACTION; MULTI-DIMENSIONAL SCALING; OUTPUT QUALITY; POINT CLOUD; PROJECTION PRECISION; PROJECTION TECHNIQUES; VISUAL ANALYSIS; VISUAL ASSESSMENTS; VISUAL MAPPING; VISUALIZATION SYSTEM;

COMPUTER AIDED LANGUAGE TRANSLATION; CONFORMAL MAPPING; DATA REDUCTION; HUMAN COMPUTER INTERACTION; PRINCIPAL COMPONENT ANALYSIS; VISUALIZATION;

DATA VISUALIZATION;

EID: 77956685977     PISSN: 14738716     EISSN: 14738724     Source Type: Journal    
DOI: 10.1057/ivs.2010.2     Document Type: Conference Paper

References (24)

1. Dhillon, I., Modha, D. and Spangler, W. (2002) Class visualization of high-dimensional data with applications. Computational Statistics and Data Analysis 4(1): 59-90.
2. Jolliffe, I. (2002) Principal Components Analysis. 3rd edn, Washington: Springer.
3. Cox, M. and Cox, M. (2001) Multidimensional Scaling. Dordrecht, Netherlands: Chapman and Hall.
4. Sammon, J.W. (1969) A nonlinear mapping for data structure analysis. IEEE Transactions on Computers 18: 401-409.
5. Kohonen, T. (2001) Self-organizing Maps. 3rd edn, Berlin, Heidelberg, New York: Springer.
6. Wilkinson, L., Anand, A. and Grossman, R. (2005) Graphtheoretic scagnostics. In: M.Ward, and J. Stasko (eds.) Proceedings of IEEE Symposium on Information Visualization. Washington: IEEE Computer Society, pp. 157-164.
7. Sips, M., Neubert, B., Lewis, J.P. and Hanrahan, P. (2009) Selecting good views of high-dimensional data using class consistency. Computer Graphics Forum 28(3): 831-838.
8. Tatu, A. et al. (2009) Combining automated analysis and visualization techniques for effective exploration of highdimensional data. In: J. van Wijk, and J. Stasko (eds.) Proceedings of IEEE Symposium on Visual Analytics Science and Technology. Washington: IEEE Computer Society, pp. 59-66.
9. Seifert, C., Saboland, V. and Kienreich, W. (2010). StressMaps: Analysing local phenomena in dimensionality reduction based visualisations. In: J. Kohlhammer and D. Keim (eds.) Proceedings of International Symposium on Visual Analytics Science and Technology 2010. Braunschweig: European Association for Computer Graphics.
10. Hopf, M. and Ertl, T. (2003) Hierarchical splatting of scattered data. In: P.C. Wong, and K. Gaither (eds.) VIS '03: Proceedings of the 14th IEEE Visualization 2003 (VIS'03). Washington DC, USA: IEEE Computer Society, p. 57.
11. Williams, M. and Munzner, T. (2004) Steerable, progressive multidimensional scaling. INFOVIS '04: Proceedings of the IEEE Symposium on Information Visualization. Washington DC, USA: IEEE Computer Society, pp. 57-64.
12. Sanftmann, H. and Weiskopf, D. (2009) Illuminated 3D Scatterplots. Computer Graphics Forum (Proceedings of Euro Vis 2009) 28(3): 751-758.
13. Bachthaler, S. and Weiskopf, D. (2008) Continuous scatterplots. IEEE Transactions on Visualization and Computer Graphics 14(6): 1428-1435.
14. Rousseeuw, P., Ruts, I. and Tukey, J. (1999) The bagplot: A bivariate boxplot. The American Statistician 53(4): 382-387.
15. Schreck, T. and Panse, C. (2007) A new metaphor for projectionbased visual analysis and data exploration. In: R. Erbacher, K. Boerner, M. Groehn, and J. Roberts (eds.) Proceedings of IS&T/SPIE Conference on Visualization and Data Analysis. Bellingham: SPIE, p. 64950L.
16. Schreck, T., Schuessler, M., Zeilfelder, F. and Worm, K. (2008) Butterfly plots for visual analysis of large point cloud data. In: V. Skala (ed.), Proceedings of International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision. UNION Agency: Plzen.
17. Pang, A., Wittenbrink, C. and Lodha, S. (1997) Approaches to uncertainty visualization. The Visual Computer 13(8): 370-390.
18. Johnson, C.R. and Sanderson, A.R. (2003) A next step: Visualizing errors and uncertainty. IEEE Computer Graphics and Applications 23(5): 6-10.
19. MacEachren, A.M. et al. (2005) Visualizing geospatial information uncertainty: What we know and what we need to know. Cartography and Geographic Information Science 32(3): 139-160.
20. Griethe, H. and Schumann, H. (2006) The visualization of uncertain data: Methods and problems. In: T. Schulze, G. Horton, B. Preim and S. Schlechtweg (eds.) Proceedings of Simulation und Visualisierung. Erlangen: SCS Europe, pp. 143-156.
21. Blake, C. and Merz, C. (1998) UCI repository of machine learning databases, www.ics.uci.edu/?mlearn/.
22. Schreck, T., Tekušová, T., Kohlhammer, J. and Fellner, D. (2007) Trajectory-based visual analysis of large financial time series data. SIGKDD Explorations 9(2): 30-37.
23. Elmqvist, N., Dragicevic, P. and Fekete, J.-D. (2008) Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation. IEEE Transactions on Visualization and Computer Graphics 14(6): 1148-1539.
24. Hinneburg, A., Aggarwal, C.C. and Keim, D.A. (2000) What is the nearest neighbor in high dimensional spaces?. VLDB, pp. 506-515.