Empirical guidance on scatterplot and dimension reduction technique choices

IEEE Transactions on Visualization and Computer Graphics

Volumn 19, Issue 12, 2013, Pages 2634-2643

  Sedlmair, Michael ^a   Munzner, Tamara ^b   Tory, Melanie ^c  

^a UNIVERSITY OF VIENNA   (Austria)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^c UNIVERSITY OF VICTORIA   (Canada)

Author keywords

Dimensionality reduction; quantitative study; scatterplots

Indexed keywords

CLUSTERING ALGORITHMS; REDUCTION;

CLUSTER SEPARATION; DIMENSION REDUCTION; DIMENSION REDUCTION TECHNIQUES; DIMENSIONALITY REDUCTION; EXPLORATION PROCESS; HIGH DIMENSIONAL DATA; QUANTITATIVE STUDY; SCATTER PLOTS;

DATA REDUCTION;

ALGORITHM; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER GRAPHICS; COMPUTER INTERFACE; COMPUTER SIMULATION; IMAGE ENHANCEMENT; PROCEDURES; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING; ARTICLE; METHODOLOGY;

ALGORITHMS; COMPUTER GRAPHICS; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; USER-COMPUTER INTERFACE;

EID: 84886643002     PISSN: 10772626     EISSN: None     Source Type: Journal    
DOI: 10.1109/TVCG.2013.153     Document Type: Article

References (53)

1. A. Anand, L. Wilkinson, and T. N. Dang. Visual Pattern Discovery using Random Projections. In Proc. IEEE Conf. Visual Analytics Science and Technology (VAST), pages 43-52, 2012.
2. M. Belkin and P. Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in neural information processing systems, 14:585-591, 2001.
3. E. Bertini and G. Santucci. Visual Quality Metrics. In Proc. AVI Workshop on BEyond time and errors: novel evaluation methods for information visualization (BELIV). ACM, 2006.
4. E. Bertini, A. Tatu, and D. A. Keim. Quality metrics in high-dimensional data visualization: an overview and systematization. IEEE Trans. on Visualization and Computer Graphics (InfoVis), 17(12):2203-2212, 2011.
5. I. Borg and P. Groenen. Modern multidimensional scaling: Theory and applications. Springer, 2005.
6. M. Chalmers. Using a landscape metaphor to represent a corpus of documents. In Proc. European Conf. Spatial Information Theory (COSIT '93), pages 377-390. Springer LNCS 716, 1993.
7. K. Charmaz. Constructing grounded theory. A practical guide through qualitative analysis. Sage Publications, Inc, 2006.
8. A. Cockburn and B. McKenzie. An evaluation of cone trees. In People and Computers XIV: Usability or Else. British Computer Society Conf. on Human Computer Interaction, pages 425-436. Springer, 2000.
9. A. Cockburn and B. McKenzie. 3D or not 3D: evaluating the effect of the third dimension in a document management system. In Proc. ACM Conf. Human Factors in Computing Systems (CHI), pages 434-441, 2001. (Pubitemid 32393386)
10. A. Cockburn and B. McKenzie. Evaluating the effectiveness of spatial memory in 2D and 3D physical and virtual environments. In Proc. ACM Conf. Human Factors in Computing Systems (CHI), pages 203-210, 2002.
11. J.W. Creswell and V. L. P. Clark. Designing and conducting mixed methods research. Wiley Online Library, 2007.
12. I. Doyle, M. Ratcliffe, A. Walding, E. V. Bon, M. Dymond, W. Tomlinson, D. Tilley, P. Shelton, and I. Dougall. Differential gene expression analysis in human monocyte-derived macrophages: impact of cigarette smoke on host defence. Molecular immunology, 47(5):1058, 2010.
13. A. L. Duran, J. Yang, L.Wang, and L.W. Sumner. Metabolomics spectral formatting, alignment and conversion tools (msfacts). Bioinformatics, 19(17):2283-2293, 2003. (Pubitemid 37483984)
14. S. Fabrikant. Spatial metaphors for browsing large data archives. PhD thesis, University of Colorado Boulder, 2000.
15. R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Human Genetics, 7(2):179-188, 1936.
16. S. L. France and J. Carroll. Two-way multidimensional scaling: A review. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 41(5):644-661, 2011.
17. A. Frank and A. Asuncion. University of California Irvine (UCI) Machine Learning Repository, 2010.
18. D. Furniss, A. Blandford, and P. Curzon. Confessions from a Grounded Theory PhD: Experiences and Lessons Learnt. In ACM Trans. Computer-Human Interaction (ToCHI), pages 113-122, 2011.
19. C. Holt and M. Bradford. Evaluating benchmarks of population status for Pacific salmon. North American Journal of Fisheries Management, 31(2):363-378, 2011.
20. S. Ingram, T. Munzner, V. Irvine, M. Tory, S. Bergner, and T. M̈oller. DimStiller: Workflows for dimensional analysis and reduction. In Proc. IEEE Conf. Visual Analytics Science and Technology (VAST), 2010.
21. S. Ingram, T. Munzner, and M. Olano. Glimmer: Multilevel MDS on the GPU. IEEE Trans. Visualization and Computer Graphics (TVCG), 15(2):249-261, 2009.
22. I. T. Jolliffe. Principal Component Analysis, 2nd ed. Springer, 2002.
23. H. S. Kim, J. P. Schulze, A. C. Cone, G. E. Sosinsky, and M. E. Martone. Dimensionality reduction on multi-dimensional transfer functions for multi-channel volume data sets. Information visualization, 9(3):167-180, 2010.
24. K. Krippendorff. Content Analysis: An Introduction to Its Methodology. Sage, 2004.
25. J. Kruskal and M. Wish. Multidimensional Scaling. Sage, 1978.
26. H. Lam. A framework of interaction costs in information visualization. IEEE Trans. Visualization and Computer Graphics (InfoVis), 14(6):1149-1156, 2008.
27. J. M. Lewis, M. Ackerman, and V. de Sa. Human cluster evaluation and formal quality measures: A comparative study. In Proc. 34th Conf. of the Cognitive Science Society (CogSci), pages 1870-1875, 2012.
28. J. M. Lewis, L. van der Maaten, and V. de Sa. A behavioral investigation of dimensionality reduction. In Proc. 34th Conf. of the Cognitive Science Society (CogSci), pages 671-676, 2012.
29. J. Li, J.-B. Martens, and J. J. van Wijk. Judging correlation from scatterplots and parallel coordinate plots. Information Visualization, 9(1):13-30, 2008.
30. G. Newby. Empirical study of a 3D visualization for information retrieval tasks. J. Intelligent Information Systems, 18(1):31-53, 2002. (Pubitemid 34129946)
31. R. Rensink and G. Baldridge. The perception of correlation in scatterplots. Computer Graphics Forum (EuroVis), 29(3):1203-1210, 2010.
32. T. Sando, M. Tory, and P. Irani. Effects of animation, user-controlled interactions, and multiple static views in understanding 3d structures. In Proc. Symp. Applied Perception in Graphics and Visualization (APGV), pages 69-76, 2009.
33. SAP. HANA, 2010. http://www.sap.com/hana/, last accessed 01/10.
34. M. Sedlmair, M. Brehmer, S. Ingram, and T. Munzner. Dimensionality reduction in the wild: Gaps and guidance. Technical report, Dept. of Computer Science, University of British Columbia, 2012.
35. M. Sedlmair, A. Tatu, M. Tory, and T. Munzner. A taxonomy of visual cluster separation factors. Computer Graphics Forum (EuroVis), 31(3):1335-1344, 2012.
36. M. Sips, B. Neubert, J. P. Lewis, and P. Hanrahan. Selecting good views of high-dimensional data using class consistency. Computer Graphics Forum (EuroVis), 28(3):831-838, 2009.
37. M. St. John, M. B. Cowen, H. S. Smallman, and H. M. Oonk. The use of 2-D and 3-D displays for shape understanding versus relative position tasks. Human Factors, 43(1):79-98, 2001.
38. A. Tatu, G. Albuquerque, M. Eisemann, J. Schneidewind, H. Theisel, M. Magnor, and D. Keim. Combining automated analysis and visualization techniques for effective exploration of high-dimensional data. In Proc. IEEE Symp. Visual Analytics Science and Technology (VAST), pages 59-66, 2009.
39. V. Todorov and P. Filzmoser. An object oriented framework for robust multivariate analysis. Journal of Statistical Software, 32(3):1-47, 2009.
40. M. Tory, D. Sprague, F. Wu, W. Y. So, and T. Munzner. Spatialization design: comparing points and landscapes. IEEE Trans. on Visualization and Computer Graphics (InfoVis), 13(6):1262-9, 2007. (Pubitemid 350076598)
41. M. Tory, C. Swindells, and R. Dreezer. Comparing dot and landscape spatializations for visual memory differences. IEEE Trans. on Visualization and Computer Graphics (InfoVis), 15(6):1033-9, 2009.
42. University of Massachusetts. Statistical Data and Software Help, 2011. http://www.umass.edu/statdata/statdata/, last accessed 11/11.
43. L. van der Maaten. Learning a parametric embedding by preserving local structure. Proc. Int. Conf. on Artificial Intelligence and Statistics (AISTATS), 5:384-391, 2009.
44. L. van der Maaten and G. Hinton. Visualizing data using t-SNE. Journal of Machine Learning Research, 9(2579-2605):85, 2008.
45. L. Van der Maaten, E. Postma, and H. Van Den Herik. Dimensionality reduction: A comparative review. Journal of Machine Learning Research, 10:1-41, 2009.
46. J. J. van Wijk. Views on visualization. IEEE Trans. Visualization and Computer Graphics (TVCG), 12(2):421-432, 2006. (Pubitemid 43824556)
47. VisuMap Technologies Inc. VisuMap Data Repository, 2011. http://www.visumap.net/, last accessed 11/11.
48. M. O. Ward. Xmdv data repository, 2011. http://davis.wpi.edu/xmdv/ datasets.html, last accessed 11/11.
49. C.Ware. Designing with a 2 1/2 D Attitude. Information Design Journal, 10(3):255-262, 2001.
50. S. Westerman. Browsing a document collection represented in two-and three-dimensional virtual information space. Intl. Journal Human Computer Studies (IJHCS), 62(6):713-736, 2005. (Pubitemid 40744158)
51. S. Westerman and T. Cribbin. Mapping semantic information in virtual space: dimensions, variance and individual differences. Intl. Journal Human Computer Studies (IJHCS), 53(5):765-787, 2000. (Pubitemid 32032820)
52. C. Wickens and E. Merwin, D.H. Lin. Implications of graphics enhancements for the visualization of scientific data: Dimensional integrality, stereopsis, motion, and mesh. Human Factors, 36(1):44-61, 1994. (Pubitemid 24177075)
53. L. Wilkinson and A. Anand. Graph-theoretic scagnostics. Proc. IEEE Symp. Information Visualization (InfoVis), pages 157-164, 2005. (Pubitemid 44500051)