Exploratory data analysis with categorical variables: An improved rank-by-feature framework and a case study

International Journal of Human-Computer Interaction

Volumn 23, Issue 3, 2007, Pages 287-314

  Seo, Jinwook ^a   Gordish Dressman, Heather ^a  

^a CHILDREN'S NATIONAL MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; GENE EXPRESSION; INFORMATION ANALYSIS;

BIO-MEDICAL; CATEGORICAL VARIABLES; CLUSTERING RESULTS; EXPLORATORY DATA ANALYSIS; GENE EXPRESSION DATA; LONGITUDINAL CASE STUDY; MULTI-DIMENSIONAL DATASETS; STOCK PERFORMANCE;

DATA HANDLING;

EID: 38849157947     PISSN: 10447318     EISSN: 15327590     Source Type: Journal    
DOI: 10.1080/10447310701702519     Document Type: Article

References (32)

1. Abello, J., & van Ham, F. (2004). Matrix Zoom: A visual interface to semi-external graphs. In Proceedings of IEEE Symposium on Information Visualization (pp. 183-190). Austin, TX: IEEE Computer Society Press.
2. Blasius, J., & Greenacre, M. L. (1998). Visualization of categorical data. San Diego, CA: Academic.
3. Di Giovanni, S., Faden, A. I., Yakovlev, A., Duke-Cohan, J. S., Finn, T., Thouin, M., et al. (2005). Neuronal plasticity after spinal cord injury: identification of a gene cluster driving neurite outgrowth. The FASEB Journal, 19(1), 153-154.
4. Elomaa, T., & Rousu, J. (1999). General and efficient multisplitting of numerical attributes. Machine Learning, 36(3), 201-244.
5. Friendly, M. (2000). Visualizing Categorical Data. Cary, NC: SAS Publishing.
6. Gibson, D., Kleinberg, J. M., & Raghavan, P. (2000). Clustering categorical data: an approach based on dynamical systems. VLDB Journal, 8(3-4), 222-236.
7. Gonzales, V., & Kobsa, A. (2003). A workplace study of the adoption of information visualization systems. Proceedings of the I-KNOW'03: Third International Conference on Knowledge Management, 92-102.
8. Guha, S., Rastogi, R., & Shim, K. (1999). ROCK: A robust clustering algorithm for categorical attributes. Proceedings of the 15th International Conference on Data Engineering, 512-521.
9. Guo, D., Gahegan, M., Peuquet, D., & MacEachren, A. (2003). Breaking down dimensionality: an effective feature selection method for high-dimensional clustering. Proceedings of the Third SIAM International Conference on Data Mining, Workshop on Clustering High Dimensional Data and its Applications, 29-42.
10. Henry, N., & Fekete, J.-D. (2006). MatrixExplorer: A dual-representation system to explore social networks. IEEE Transactions on Visualization and Computer Graphics, 12(5), 677-684.
11. Kincaid, R. (2004). VistaClara: An interactive visualization for exploratory analysis of DNA microarrays. In Proceedings of the ACM Symposium on Applied Computing (pp. 167-174). Nicosia, Cyprus: ACM Press.
12. Kules, B., Shneiderman, B., & Plaisant, C. (2003). Data exploration with paired hierarchical visualizations: Initial designs of PairTrees. Proceedings of the National Conference on Digital Government Research, 255-260.
13. Liu, H., & Motoda, H. (1998). Feature selection for knowledge discovery and data mining. Boston: Kluwer Academic.
14. Mackinlay, J. (1986). Automating the design of graphical presentations of relational information. A CM Transactions on Graphics, 5(2), 110-141.
15. Moore, D. S., & McCabe, G. P. (1999). Introduction to the practice of statistics (3rd ed.). New York: W.H. Freeman.
16. Munzner, T., Guimbretiere, F., Tasiran, S., Zhang, L., & Zhou, Y. (2003). Treejuxtaposer: Scalable tree comparison using Focus+Context with guaranteed visibility. ACM Transactions on Graphics, 22(3), 453-462.
17. Press, W. H., Flannery, B. P., Teukolsky, S. A., & Vetterling, W. T. (1992). Numerical recipes in C : The art of scientific computing. Cambridge, UK: Cambridge University Press.
18. Rijsbergen, C. J. V. (1979). Information retrieval (2nd ed.). London: Butterworth.
19. Rosario, G. E., Rundensteiner, E. A., Brown, D. C., Ward, M. O., & Huang, S. (2004). Mapping nominal values to numbers for effective visualization. Information Visualization, 3(2), 80-95.
20. Saraiya, P., North, C., Lam, V., & Duca, K. (2006). An insight-based longitudinal study of visual analytics. IEEE Transactions on Visualization and Computer Graphics, 12(6), 1511-1522.
21. Seo, J., Bakay, M., Chen, Y.-W., Hilmer, S., Shneiderman, B., & Hoffman, E. P. (2004). Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays. Bioinformatics, 20(16), 2534-2544.
22. Seo, J., Gordish-Dressman, H., & Hoffman, E. P. (2006). An interactive power analysis tool for microarray hypothesis testing and generation. Bioinformatics, 22(7), 808-814.
23. Seo, J., & Hoffman, E. (2006). Probe set algorithms: Is there a rational best bet? BMC Bioinformatics, 7(1), 395.
24. Seo, J., & Shneiderman, B. (2005a). A knowledge integration framework for information visualization. Lecture Notes in Computer Science, 3379, 207-220.
25. Seo, J., & Shneiderman, B. (2005b). A rank-by-feature framework for interactive exploration of multidimensional data. Information Visualization, 4(2), 99-113.
26. Seo, J., & Shneiderman, B. (2006). Knowledge discovery in high-dimensional data: case studies and a user survey for the rank-by-feature framework. IEEE Transactions on Visualization and Computer Graphics, 12(3), 311-322.
27. Siirtola, H., & Makinen, E. (2005). Constructing and reconstructing the reorderable matrix. Information Visualization, 4(1), 32-48.
28. Thompson, P. D., Moyna, N., Seip, R., Price, T., Clarkson, P., Angelopoulos, T., et al. (2004). Functional polymorphisms associated with human muscle size and strength. Medicine & Science in Sports & Exercise, 36(7), 1132-1139.
29. Tukey, J. W., & Tukey, P. A. (1985). Computer graphics and exploratory data analysis: An introduction. In Proceedings of Annual Conference and Exposition: Computer Graphics (Vol. 3, pp. 773-785). Fairfax, VA: National Micrograhics Association.
30. Yang, J., Peng, W., Ward, M. O., & Rundensteiner, E. A. (2003). Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional data sets. In Proceedings of IEEE Symposium on Information Visualization (pp. 105-112). Seattle, WA: IEEE Computer Society Press.
31. Zhao, P., Seo, J., Wang, Z., Wang, Y., Shneiderman, B., & Hoffman, E. P. (2003). In vivo filtering of in vitro expression data reveals MyoD targets. Comptes Rendus Biologies, 326(10-11), 1049-1065.
32. Ziegler, J., Kunz, C., & Botsch, V. (2002). Matrix browser: visualizing and exploring large networked information spaces. In Proceedings of CHI '02 Extended Abstracts on Human Factors in Computing Systems (pp. 602-603). Minneapolis, MN: ACM Press.