Visual Analytics: Definition, Process, and Challenges - Information Visualization: Human-Centered Issues and Perspectives - page 174

18. Han, J., Kamber, M. (eds.): Data Mining: Concepts and Techniques. Morgan
Kaufmann, San Francisco (2000)
19. Hand, D., Mannila, H., Smyth, P. (eds.): Principles of Data Mining. MIT Press,
Cambridge (2001)
20. Inselberg, A., Dimsdale, B.: Parallel Coordinates: A Tool for Visualizing Multi-
variate Relations (chapter 9), pp. 199-233. Plenum Publishing Corporation, New
York (1991)
21. Jacko, J.A., Sears, A.: The Handbook for Human Computer Interaction. Lawrence
Erlbaum & Associates, Mahwah (2003)
22. Johnson, C., Hanson, C. (eds.): Visualization Handbook. Kolam Publishing (2004)
23. Keim, D., Ertl, T.: Scientific visualization (in german). Information Technol-
ogy 46(3), 148-153 (2004)
24. Keim, D., Ward, M.: Visual Data Mining Techniques (chapter 11). Springer, New
York (2003)
25. Keim, D.A., Ankerst, M., Kriegel, H.-P.: Recursive pattern: A technique for visu-
alizing very large amounts of data. In: VIS '95: Proceedings of the 6th conference
on Visualization '95, Washington, DC, USA, p. 279. IEEE Computer Society
Press, Los Alamitos (1995)
26. Keim, D.A., Panse, C., Sips, M., North, S.C.: Pixel based visual data mining of
geo-spatial data. Computers &Graphics 28(3), 327-344 (2004)
27. Kerren, A., Stasko, J.T., Fekete, J.-D., North, C.J. (eds.): Information Visualiza-
tion. LNCS, vol. 4950. Springer, Heidelberg (2008)
28. Kruger, J., Schneider, J., Westermann, R.: Clearview: An interactive context pre-
serving hotspot visualization technique. IEEE Transactions on Visualization and
Computer Graphics 12(5), 941-948 (2006)
29. Maimon, O., Rokach, L. (eds.): The Data Mining and Knowledge Discovery Hand-
book. Springer, Heidelberg (2005)
30. Meliou, A., Chu, D., Guestrin, C., Hellerstein, J., Hong, W.: Data gathering tours
in sensor networks. In: IPSN (2006)
31. Mitchell, T.M.: Machine Learning. McGraw-Hill, Berkeley (1997)
32. Naumann, F., Bilke, A., Bleiholder, J., Weis, M.: Data fusion in three steps:
Resolving schema, tuple, and value inconsistencies. IEEE Data Eng. Bull. 29(2),
21-31 (2006)
33. North, C.: Toward measuring visualization insight. IEEE Comput. Graph.
Appl. 26(3), 6-9 (2006)
34. Perner, P. (ed.): Data Mining on Multimedia Data. LNCS, vol. 2558. Springer,
Heidelberg (2002)
35. Schumann, H., Muller, W.: Visualisierung - Grundlagen und allgemeine Metho-
den. Springer, Heidelberg (2000)
36. Shneiderman, B.: Tree visualization with tree-maps: 2-d space-filling approach.
ACM Trans. Graph. 11(1), 92-99 (1992)
37. Shneiderman, B., Plaisant, C.: Designing the User Interface. Addison-Wesley,
Reading (2004)
38. Spence, R.: Information Visualization. ACM Press, New York (2001)
39. Thomas, J.J., Cook, K.A.: Illuminating the Path. IEEE Computer Society Press,
Los Alamitos (2005)
40. Tricoche, X., Scheuermann, G., Hagen, H.: Tensor topology tracking: A visual-
ization method for time-dependent 2d symmetric tensor fields. Comput. Graph.
Forum 20(3) (2001)
41. Unwin, A., Theus, M., Hofmann, H.: Graphics of Large Datasets: Visualizing a
Million (Statistics and Computing). Springer, New York (2006)