References
1 Baker, MP, Bushell, C. After the storm: considerations for information visualization. IEEE Comput Graph Appl 1995, 15: 12–15.
2 Wise, JA, Thomas, JJ, Pennock, K, Lantrip, D, Pottier, M, Schur, A, Crow, V.
Visualizing the non‐visual: Spatial analysis and interaction with information from text documents.
Proceedings of IEEE Symposium on Information Visualization `95, Atlanta, Georgia, USA, 1995; 51–58.
3 Chi, EH. A taxonomy of visualization techniques using the data state reference model. Proceedings of InfoVis, 2000; Salt Lake City, UT, October 9–10, 2000, (pp. 69–75) .
4 Gaviria, AR. When is information visualization art? determining the critical criteria. Leonardo 2008, 41: 479–482.
5 Bertin, J.
Semiology of Graphics: Diagrams, Networks, Maps. Wisconsin:
University of Wisconsin Press; 1983.
6 Cleveland, WS, McGill, R. Graphical perception: theory, experimentation, and application to the development of graphical methods. J Am Stat Assoc 1984, 79: 531–553.
7 Mackinlay, JD. Automating the design of graphical presentations of relational information. ACM Trans Graph 1986, 5: 110–141.
8 Chang, R, Ziemkiewicz, C, Green, TM, Ribarsky, W. Defining insight for visual analytics. IEEE Comput Graph Appl 2009, 29: 14–17.
9 North, C. Towards measuring visualization insight. IEEE Comput Graph Appl 2006, 26: 6–9.
10 Chen, C. An information‐theoretic view of visual analytics. IEEE Comput Graph Appl 2008, 28: 18–23.
11 Heer, J, Viegas, FB, Wattenberg, M. Voyagers and voyeurs: supporting asynchronous collaborative visualization. Commun ACM 2009, 52: 87–97.
12 Bush, V. As we may think. Atlantic Monthly 1945, 176: 101–108.
13 Plaisant, C, Fekete, J, Grinstein, G. Promoting insight based evaluation of visualizations: from contest to benchmark repository. IEEE Trans Vis Comput Graph 2008, 14: 120–134.
14 Friendly, M. %22A brief history of data visualization%22. In: Chen, C, Härdle, W, Unwin, A, eds.
Handbook of Computational Statistics: Data Visualization, vol. 3. Heidelberg:
Springer; 2007; 1–34.
15 Friendly, M, Denis, DJ. (2001, Oct 16, 2008). Milestones in the history of thematic cartography, statistical graphics, and data visualization: An illustrated chronology of innovations Retrieved March 30, 2009, from .
16 Furnas, GW.
Generalized fisheye views. In
Proceedings of CHI `86, 1986, 16–23.
17 Tufte, ER.
The Visual Display of Quantitative Information. Cheshire, CT:
Graphics Press; 1983.
18 Inselberg, A. The Plane with Parallel Coordinates. Visual Computer 1985, 1: 69–91.
19 Wegman, E. Hyperdimensional Data Analysis Using Parallel Coordinates. J Am Stat Assoc 1990, 85: 664–675.
20 Johnson, B, Shneiderman, B.
Tree‐maps: A space filling approach to the visualization of hierarchical information structures. In Proceedings of IEEE Visualization 1991, 91, 284–291.
21 Eades, P. A heuristic for graph drawing. Congressus Numerantium 1984, 42: 149–160.
22 Fruchterman, TMJ, Reingold, EM. Graph drawing by force‐directed placement. Software‐ Practice and Experience 1991, 21: 1129–1164.
23 Kamada, T, Kawai, S. A general framework for visualizing abstract objects and relations. ACM Trans Graph 1991, 10: 1–39.
24 Dwyer, T, Marriott, K, Schreiber, F, Stuckey, PJ, Woodward, M, Wybrow, M. Exploration of networks using overview + detail with constraint‐based cooperative layout. IEEE Trans Vis Comput Graph 2008, 14: 1293–1300.
25 Fairchild, K, Poltrock, S, Furnas, G. %22SemNet: Three‐dimensional graphic representations of large knowledge bases%22. In: Guidon, R, ed.
Cognitive Science and its Applications for Human‐Computer Interaction Hillsdale, NJ:
Lawrence Erlbaum Associates; 1988; 201–233.
26 Robertson, GG, Mackinlay, JD, Card, SK.
Cone trees: Animated 3D visualizations of hierarchical information. In Proceedings of CHI `91, New Orleans, LA, 1991, 189–194.
27 Eick, SG, Wills, GJ.
Navigating large networks with hierarchies. In Proceedings of the IEEE Conference on Visualization, Los Alamitos, CA, 1993.
28 Wills, GJ. NicheWorks: Interactive visualization of very large graphs Retrieved 4th August, 1998, from .
29 Chuah, MC, Roth, SF, Kolojejchick, J, Mattis, J, Juarez, O.
SageBook: Searching data‐graphics by content. In Proceedings of CHI `95, Denver, CO, 1995, 338–345.
30 Kohonen, T.
Self‐Organizing Maps:
Springer; 1995.
31 Lamping, J, Rao, R, Pirolli, P.
A focus + context technique based on hyperbolic geometry for visualizing large hierarchies. In
Proceedings of ACM CHI`95 Conference on Human Factors in Computing Systems, 1995, 401–408.
32 Munzner, T. %22H3: Laying out large directed graphs in 3D hyperbolic space%22. In: Dill, J, Gershon, N, eds.
Proceedings of the 1997 IEEE Symposium on Information Visualization. Phoenix:
IEEE CS Press 1997; 2–10.
33 Card, S, Mackinlay, J, Shneiderman, B, eds.
Readings in Information Visualization: Using Vision to Think. San Francisco, CA:
Morgan Kaufmann; 1999.
34 Chen, C.
Information Visualisation and Virtual Environments. London:
Springer; 1999.
35 Spence, B.
Information Visualization. New York:
Addison‐Wesley; 2000.
36 Ware, C.
Information Visualization: Perception for Design. San Francisco:
Morgan Kaufmann Publishers; 2000.
37 Thomas, J, Cook, K.
Illuminating the Path, the Research and Development Agenda for Visual Analytics. Phoenix:
IEEE CS Press; 2005.
38 Chen, C, Paul, RJ. Visualizing a knowledge domain`s intellectual structure. Computer 2001, 34: 65–71.
39 Bollen, J, Sompel, HVd, Hagberg, A, Bettencourt, L, Chute, R, Rodriguez, MA, Balakireval, L. Clickstream data yields high‐resolution maps of science. PLoS ONE 2009, 4: e4803.
40 Eccles, R, Kapler, T, Harper, R, Wright, W. Stories in GeoTime. Inf Vis 2008, 7: 3–17.
41 MacEachren, AM.
How Maps Work: Representation, Visualization, and Design. New York:
Guilford Press; 1995.
42 Chalmers, M. Bead—an information visualization system. Proc Asis Annu Meet 1995, 32: 249–249.
43 Jerding, DF, Stasko, JT. The information mural: a technique for displaying and navigating large information spaces. IEEE Trans Vis Comput Graph 1998, 4: 257–271.
44 Shneiderman, B.
The eyes have it: A task by data type taxonomy for information visualization. In Proceedings of IEEE Workshop on Visual Language, Boulder, CO, 1996, 336–343.
45 Lehrer, J. The Eureka Hunt: why do good ideas come to us when they do? The New Yorker, July 28, 2008; 40–45.
46 Sternberg, RJ, Davidson, JE, eds.
The Nature of Insight.
MIT Press; 1996.
47 Chen, C, Chen, Y, Horowitz, M, Hou, H, Liu, Z et al. Towards an explanatory and computational theory of scientific discovery. J Informetrics 2009, 3: 191–209.
48 Plaisant, C, Fekete, JD, Grinstein, G. Promoting insight‐based evaluation of visualizations: from contest to benchmark repository. IEEE Trans Vis Comput Graph 2008, 14: 120–134.
49 Lam, H. A framework of interaction costs in information visualization. IEEE Trans Vis Comput Graph 2008, 14: 1149–1156.
50 Norman,, DA. The Design of Everyday Things. New York:
Basic Books; 2002.
51 Chen, C, Yu, Y. Empirical studies of information visualization: a meta‐analysis. Int J Hum Comput Stud 2000, 53: 851–866.
52 Julien, CA, Leide, JE, Bouthillier, F. Controlled user evaluations of information visualization interfaces for text retrieval: Literature review and meta‐analysis. J Am Soc Inform Sci Technol 2008, 59: 1012–1024.
53 Kerren, A, Stasko, JT, Fekete, J‐D, North, C. Workshop report: information visualization—human‐centered issues in visual representation, interaction, and evaluation. Inf Vis 2007, 6: 189–196.
54 Liu, ZC, Nersessian, NJ, Stasko, JT. Distributed Cognition as a Theoretical Framework for Information Visualization. IEEE Trans Vis Comput Graph 2008, 14: 1173–1180.
55 Eick, SG. Information visualization at 10. IEEE Comput Graph Appl 2005, 25: 12–14.
56 Gray, AG, Moore, AW. N‐body problems in statistical learning. Adv Neural Inf Process Syst 2001, 13: 521–527.
57 Moore, A. %22Very fast EM‐based mixture model clustering using multiresolution kd‐trees%22. In: Kearns, M, Cohn, D, eds.
Advances in Neural Information Processing Systems:
Morgan Kaufman; 1999; 543–549.
58 Holten, D. Hierarchical edge bundles: visualization of adjacency relations in hierarchical data. IEEE Trans Vis Comput Graph 2006, 12: 741–748.
59 Cui, W, Zhou, H, Qu, H, Wong, PC, Li, X. Geometry‐based edge clustering for graph visualization. IEEE Trans Vis Comput Graph 2008, 14: 1277–1284.
60 Dwyer, T, Koren, Y, Marriott, K. IPSep‐CoLa: An Incremental Procedure for Separation Constraint Layout of Graphs. IEEE Trans Vis Comput Graph 2006, 12: 821–828.
61 Bottger, J, Balzer, M, Deussen, O. Complex logarithmic views for small details in large contexts. IEEE Trans Vis Comput Graph 2006, 12: 845–852.
62 Luboschik, M, Schumann, H, Cords, H. Particle‐based labeling: fast point‐feature labeling without obscuring other visual features. IEEE Trans Vis Comput Graph 2008, 14: 1237–1244.
63 Chen, C. Tracking latent domain structures: an integration of pathfinder and latent semantic analysis. AIDS Soc 1997, 11: 48–62.
64 Chen, C, Morris, S.
Visualizing evolving networks: Minimum spanning trees versus Pathfinder networks. In
Proceedings of IEEE Symposium on Information Visualization, Seattle, Washington, 2003, 67–74.
65 Quirin, A, Cordon, O, Santamaria, J, Vargas‐Quesada, B, Moya‐Anegon, F. A new variant of the Pathfinder algorithm to generate large visual science maps in cubic time. Inform Process Manage 2008, 44: 1611–1623.
66 Schvaneveldt, RW, ed.
Pathfinder Associative Networks: Studies in Knowledge Organization. Norwood, NJ:
Ablex Publishing Corporations; 1990.
67 Healey, CG, Kocherlakota, S, Rao, V, Mehta, R, Amant, RS. Visual perception and mixed‐initiative interaction for assisted visualization design. IEEE Trans Vis Comput Graph 2008, 14: 396–411.
68 Chen, H, Nunamaker, J
Jr, Titkova, O. Information visualization for collaborative computing. Computer 1998, 31: 75–82.
69 Isenberg, P, Carpendale, S. Interactive tree comparison for co‐located collaborative information visualization. IEEE Trans Vis Comput Graph 2007, 13: 1232–1239.
70 Wattenberg, M, Kriss, J. Designing for social data analysis. IEEE Trans Vis Comput Graph 2006, 12: 549–557.
71 Kostelnick, C. The visual rhetoric of data displays: the conundrum of clarity. IEEE Trans Professional Commun 2008, 51: 116–130.
72 Rhyne, T‐M, Hibbard, B, Johnson, C, Chen, C, Eick, S.
Can we determine the top unresolved problems of visualization? In
Proceedings of IEEE Visualization 2004, Austin, Texas, 2004, 563–566.
73 Chen, C. Top 10 unsolved information visualization problems. IEEE Comput Graph Appl 2005, 25: 12–16.
74 Johnson, CR, Moorhead, R, Munzner, T, Pfister, H, Rheingans, P et al., eds.
NIH‐NSF Visualization Research Challenges Report:
IEEE Press; 2006.
75 Munzner, T, Johnson, C, Moorhead, R, Pfister, H, Rheingans, P, Yoo, T. NIH‐NSF visualization research agenda report summary. IEEE Comput Graph Appl 2006, 26: 20–24.
76 Burkhard, RA, Andrienko, G, Andrienko, N, Dykes, J, Koutamanis, A et al. Visualization summit 2007: ten research goals for 2010. Inf Vis 2007, 6: 169–188.
77 Clauset, A, Newman, MEJ, Moore, C. Finding community structure in very large networks. Phys Rev E 2004, 70: 066111.
78 Berry, MW, Browne, M, Langville, AN, Pauca, VP, Plemmons, RJ. Algorithms and applications for approximate nonnegative matrix factorization. Comput Stat Data Anal 2007, 52: 155–173.
79 Lee, DD, Seung, HS. Learning the parts of objects by nonnegative matrix factorization. Nature 1999, 401: 788–791.
80 Dunbar, K. Concept discovery in a scientific domain. Cogn Sci 1993, 17: 397–434.
81 Soofi, ES, Retzer, JJ. Information indices: unification and applications. J Econom 2002, 107: 17–40.
82 Bartels, L. Issue voting under uncertainty: an empirical test. Am J Pol Sci 1988, 30: 709–728.
83 Itti, L, Baldi, P.
A Principled Approach to Detecting Surprising Events in Video.
Proceedings of IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2005, 631–637.