Mondrian is state-of-the-art statistical data visualization software featuring modern interactive visualization techniques for a wide range of data types. This article reviews the capabilities, functionality, and interactive properties of this software package. Key features of Mondrian are illustrated with data from the Programme for International Student Assessment (PISA) and for item analysis applications.
Cook, D., & Swayne, D. (2007). Interactive and dynamic graphics for data analysis . New York: Springer.
4.
Dykes, J. A., MacEachren, A. M., & Kraak, M.-J. (Eds.). (2005). Exploring geovisualization. Amsterdam: Elsevier.
5.
Emerson, J.W. (1998). Mosaic displays in S-Plus: A general implementation and a case study. Statistical Computing & Statistical Graphics Newsletter, 9, 17-23.
6.
Friendly, M. (1994). Mosaic displays for multi-way contingency tables . Journal of the American Statistical Association, 89, 190-200.
7.
Hartigan, J.A., & Kleiner, B. (1981). Mosaics for contingency tables. In W. F. Eddy (Ed.), Computer science and statistics: Proceedings of the thirteenth symposium on the interface (pp. 268-273). New York: Springer.
8.
Hofmann, H. (1998). Simpson on board the Titanic? Interactive methods for dealing with multivariate categorical data. Statistical Computing & Statistical Graphics Newsletter, 9, 16-19.
Hofmann, H. (2008). Mosaic plots and their variants. C.-H. Chen, W. Hardle , & A. R. Unwin (Eds.), Handbook of data visualization (pp. 617-642). Berlin, Germany: Springer.
11.
Hofmann, H., & Theus, M. (under revision). Interactive graphics for visualizing conditional densities. Journal of Computational and Graphical Statistics .
12.
Hofmann, H., & Wilhelm, A.F.X. (2001). Visual comparison of association rules. Computational Statistics, 16, 399-415.
13.
Holland, P.W., & Wainer, H. (1993). Differential item functioning. Hillsdale, NJ: Lawrence Erlbaum.
14.
Hummel, J. (1996). Linked bar charts: Analysing categorical data graphically. Computational Statistics, 11, 23-33.
15.
Inselberg, A. (1985). The plane with parallel coordinates. The Visual Computer, 1, 69-91.
16.
Inselberg, A. (1998). Visual data mining with parallel coordinates. Computational Statistics, 13, 47-63.
17.
R Development Core Team. (2006). R: A language and environment for statistical computing (ISBN 3-900051-07-0) . Vienna, Austria: R Foundation for Statistical Computing.
18.
Schneiderman, B. (1994). Dynamic queries for visual information seeking . IEEE Software, 11, 70-77.
19.
Theus, M. (2002a). Interactive data visualization using Mondrian . Statistical Computing & Statistical Graphics Newsletter , 13, 11-13.
20.
Theus, M. (2002b). Interactive data visualization using Mondrian . Journal of Statistical Software, 7(11).
21.
Theus, M., Hofmann, H., & Wilhelm, A.F.X. (1998). Selection sequences-Interactive analysis of massive data sets. Computing Science and Statistics , 29, 439-444.
22.
Theus, M., & Lauer, S.R.W. (1999). Visualizing loglinear models. Journal of Computational and Graphical Statistics, 8, 396-412.
23.
Theus, M., & Urbanek, S. (2008). Interactive graphics for data analysis. London: CRC Press.
24.
Unwin, A.R., Theus, M., & Hofmann, H. (2006). Graphics of large datasets. New York: Springer.
25.
Unwin, A.R., Volinsky, C., & Winkler, S. (2003). Parallel coordinates for exploratory modelling analysis. Computational Statistics & Data Analysis, 43, 553-564.
26.
Wegman, E.J. (1990). Hyperdimensional data analysis using parallel coordinates. Journal of the American Statistical Association , 85, 664-675.
27.
Wilkinson, L. (2005). The grammar of graphics (2nd ed.). New York: Springer.
28.
Wills, G.J. (1996). Selection: 524,288 ways to say ``this is interesting.'' Proceedings of InfoVis '96, IEEE symposium on information visualization (pp. 54-60). IEEE Computer Society Press.
29.
Young, F., Valero-Mora, P., & Friendly, M. (2006). Visual statistics: Seeing data with dynamic interactive graphics. Hoboken, NJ: Wiley.
