Mapping nominal values to numbers for effective visualization

Information Visualization

Volumn 3, Issue 2, 2004, Pages 80-95

  Rosario, Geraldine E ^a   Rundensteiner, Elke A ^a   Brown, David C ^a   Ward, Matthew O ^a   Huang, Shiping ^a  

^a Worcester Polytechnic Institute   (United States)

Author keywords

classing; clustering; correspondence analysis; dimension reduction; Nominal data; quantification; visualization

Indexed keywords

EID: 84998153410     PISSN: 14738716     EISSN: 14738724     Source Type: Journal    
DOI: 10.1057/palgrave.ivs.9500072     Document Type: Article

References (34)

1. LeBlanc J, Ward M, Wittels N. Exploring n-dimensional database. Proceeding of Visualization1990; 1990; 230–237.
2. Friendly M. Visualizing categorical data. In: Sirken MG, Herrmann DJ, Schechter S, Schwarz N, Tanur JM, Tourangeau R (Eds) Cognition and Survey Research. John Wiley & Sons Inc.: New York, 1999; 319–348.
3. Inselberg A, Dimsdale B. Parallel coordinates: a tool for visualizing multidimensional geometry. Proceedings of Visualization 1990, 1990; 361–378.
4. Ma S, Hellerstein JL. Ordering categorical data to improve visualization. In IEEE Information Visualization Symposium Late Breaking Hot Topics, 1999; 15–18.
5. Becker A, Cleveland S, Shyu J. The visual design and control of trellis display. Journal of Computational and Statistical Graphics, 1996; 5: 123–155.
6. Xmdv Tool Home Page. 2003. http://davis.wpi.edu/∼xmdv.
7. Greenacre MJ. Correspondence Analysis in Practice. Academic Press: London, 1993.
8. Johnson RA, Wichern DW. Applied Multivariate Statistical Analysis. 2nd edn. Prentice-Hall International, Inc.: New York, 1988.
9. Rosario GE, Rundensteiner EA, Brown DC, Ward MO. Mapping nominal values to numbers for effective visualization. Proceeding of information Visualization, October 2003; 113–120.
10. Riedwyl H, Schüpbach M. Parquet diagram to plot contingency tables. In: FaulbaumF, (Ed). Softstat 1993: Advances in Statistical Software. Gustav Fischer, New York, 1994; 293–299.
11. Valero-Mora PM, Young FW, Friendly M. Visualizing categorical data in vista. Computational Statistical & Data Analysis, 2003; 43 (4): 495–508.
12. Johnson B, Shneiderman B. Tree-maps: a space-filling approach to the visualization of hierarchical information structures. In: Proceeding of the Second Conference on Visualization1991. IEEE Computer Society Press: Sliver Spring, MD, 1991; 284–291.
13. Shneiderman B. Tree visualization with tree-maps: 2-d space-filling approach. ACM Trans, 1992; 11: 92–99.
14. Kolatch E, Weinstein B. Cattrees: dynamic visualization of categorical data using treemaps, 2001. http://www.cs.umd.edu/class/spring2001/cmsc838/Project/Kolatch_Weinstein.
15. Hofmann UARHG, Bernd S. Interactive graphics for data sets with missing values – manet. Journal of Computational and Graphical Statistics, 1996; 4: 113–122.
16. Pirolli P, Rao R. Table lens as a tool for making sense of data. In Proceeding of the Workshop on Advance Visual Interfaces. ACM Press: New York, 1996; 67–80.
17. Friendly M, Kwan E. Effect ordering for data displays. Computational Statistics & Data Analysis, 2003; 43: 509–539.
18. Bertin J, Scott P, Berg WJ. Graphics and Graphic Information-Processing. Water de Gruyter, Inc.: Berlin, 1982.
19. Bertin J. Semiology of Graphics. University of Wisconsin Press: Wisconsin, 1983.
20. Rao R, Card SK. The table lens: merging graphical and symbolic representations in an interactive focus+context visualization for tabular information. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM Press: New York, 1994; 318–322.
21. Beygelzimer A, Perng CS, Ma S. Fast ordering of large categorical datasets for better visualization. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press: New York, 2001; 239–244.
22. Tenenhaus M, Young FW. An analysis and synthesis of multiple correspondence analysis, optimal scaling, dual scaling homogeneity analysis and other methods for quantifying categorical multivariate data. Psychometrika, 1985; 50: 91–119.
23. Friendly M. Mosaic displays for loglinear model. Proceedings of the Statistical Graphics Section, ASA, August, 1992; 61–68.
24. Meulman J, Heiser WJ, (Eds). SPSS Categories 10.0. SPSS Inc.:, 2000.
25. Milanese R, Squire D, Pun T. Correspondence analysis and hierarchical indexing for content-based image retrieval. In: Proceeding of the Third IEEE International Conference on Image Processing, ICIP1996, 1996; 859–862.
26. Micci-Barreca D. A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems. SIGKDD Explorations, 2001; 3: 27–32.
27. Han J, Kamber M. Data Mining: Concepts and Techniques. Morgan Kaufmann: San Francisco, CA, 2001.
28. SAS Institute Inc. SAS OnlineDoc Version 8 with PDF Files, 2000.
29. StatSoft Inc. Electronic statistics text book: correspondence analysis, 2002. http://www.statsoftinc.com/textbook/stcoran.html.
30. Greenacre MJ. Theory and Applications of Correspondence Analysis. Academic Press: London, 1984.
31. Agresti A. Categorical Data Analysis. John Wiley & Sons: New York, 1990.
32. Ankersti M, Berchtold S, Keim DA. Similarity clustering of dimensions for an enhanced visualization of multidimensional data. Proceedings of the IEEE Symposium on Information Visualization, InfoVis1998, 1998; 52–60.
33. Blake C, Merz C. UCI repository of machine learning database, 1998. http://www.ics.uci.edu/∼mlearn/MLRepository.html.
34. United States Department of Health. Centers for disease control and prevention homepage, 2001. http://www.cdc.gov/.