Visual-interactive exploration of interesting multivariate relations in mixed research data sets

Computer Graphics Forum

Volumn 33, Issue 3, 2014, Pages 291-300

  Bernard, Jürgen ^a   Steiger, Martin ^a   Widmer, Sven ^b   Lücke Tieke, Hendrik ^a   May, Thorsten ^a   Kohlhammer, Jörn ^a  

^a FRAUNHOFER IGD   (Germany)

^b DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

Categories and Subject Descriptors (according to ACM CCS); I.3.6 Computer Graphics : Methodology and Techniques Interaction techniques; I.5.2 Computer Graphics : Design Methodology Pattern analysis

Indexed keywords

CLUSTERING ALGORITHMS; DISEASES;

ABSTRACTION OF DATA; ANALYSIS TECHNIQUES; DESCRIPTORS; DESIGN METHODOLOGY; I.3.6 [COMPUTER GRAPHICS]: METHODOLOGY AND TECHNIQUES - INTERACTION TECHNIQUES; INTERACTIVE EXPLORATION; STATISTICAL DEPENDENCIES; SUB-SPACE CLUSTERING;

COMPUTER GRAPHICS;

EID: 84904437541     PISSN: 01677055     EISSN: 14678659     Source Type: Journal    
DOI: 10.1111/cgf.12385     Document Type: Article

References (43)

1. Alsallakh B., Aigner W., Miksch S., Gröller M.E.,: Reinventing the Contingency Wheel: Scalable Visual Analytics of Large Categorical Data. TVCG, IEEE 18, 12 (2012), 2849-2858. 3
2. Ankerst M., Ester M., Kriegel H.-P.,: Towards an Effective Cooperation of the User and the Computer for Classification. In ACM SIGKDD (New York, NY, USA, 2000), KDD '00, ACM, pp. 179-188. 3
3. Assent I., Krieger R., Müller E., Seidl T.,: VISA: Visual Subspace Clustering Analysis. In ACM SIGKDD Explorations Special Issue on Visual Analytics, vol. 9, Issue 2 (New York, NY, USA, 2007), ACM, pp. 5-12. 3
4. Bernard J., Ruppert T., Goroll O., May T., Kohlhammer J.,: Visual-Interactive Preprocessing of Time Series Data. In SIGRAD (2012), pp. 39-48. 3
5. Bernard J., Ruppert T., Scherer M., Kohlhammer J., Schreck T.,: Content-based layouts for exploratory metadata search in scientific research data. In JCDL (New York, NY, USA, 2012), ACM, pp. 139-148. 7
6. Bernard J., Ruppert T., Scherer M., Schreck T., Kohlhammer J.,: Guided discovery of interesting relationships between time series clusters and metadata properties. In i-KNOW (New York, NY, USA, 2012), ACM, pp. 22:1-22:8. 3
7. Bremm S., von Landesberger T., Bernard J., Schreck T.,: Assisted descriptor selection based on visual comparative data analysis. In VGTC, Eurographics / IEEE (2011), EuroVis'11, Eurographics Association, pp. 891-900. 3, 7
8. Cleveland, W. S., McGill, R.,: The Many Faces of a Scatterplot. JASA 79, 388 (1984), 807-822. 3
9. Costas R., Meijer I., Zahedi Z., Wouters P.,: The Value of Research Data-Metrics for datasets from a cultural and technical point of view. A Knowledge Exchange Report (2013). URL: www.knowledge-exchange.info/datametrics. 1
10. Elmqvist N., Fekete J.-D.,: Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines. IEEE TVCG 16, 3 (2010), 439-454. 3
11. Friendly M.,: Extending mosaic displays: Marginal, conditional, and partial views of categorical data. Computational and graphical Statistics 8, 3 (1999), 373-395. 3
12. Guyon I., Elisseeff A.,: An introduction to variable and feature selection. JMLR 3 (2003), 1157-1182. 3
13. Ghoniem M., Fekete J.-D., Castagliola P.,: A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations. In Information Visualization, IEEE (Washington, DC, USA, 2004), IEEE, pp. 17-24. 6
14. Gray R.M.,: Entropy and Information Theory. Springer-Verlag Inc., New York, NY, USA, 1990, 2011. 4
15. Han J.,: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., SF, CA, USA, 2005. 3, 5
16. Hao M.C., Dayal U., Sharma R.K., Keim D.A., Janetzko H.,: Variable binned scatter plots. Information Visualization 9, 3 (2010), 194-203. 3
17. Henry N., Fekete J.-D., McGuffin M.,: NodeTrix: a Hybrid Visualization of Social Networks. Visualization and Computer Graphics, IEEE 13, 6 (2007), 1302-1309. 6
18. Hey A. J. G., Tansley S., Tolle K.M.,: The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research, 2009. 1
19. Ingram S., Munzner T., Irvine V., Tory M., Bergner S., Möller T.,: DimStiller: Workflows for dimensional analysis and reduction. In VAST (2010), pp. 3-10. 3
20. Jain A.K.,: Data Clustering: 50 Years Beyond K-means. Pattern Recogn. Lett. 31, 8 (June 2010), 651-666. 3
21. Johansson S., Jern M., Johansson J.,: Interactive Quantification of Categorical Variables in Mixed Data Sets. In Proc. of the 12th Int. Conf. on Information Visualization (2008), IV '08, IEEE Computer Society, pp. 3-10. 1, 3, 5
22. Jeong D.H., Ziemkiewicz C., Fisher B., Ribarsky W., Chang R.,: iPCA: An Interactive System for PCA-based Visual Analytics. In Proc. of the 11th Eurographics Conf. on Visualization (2009), EuroVis'09, pp. 767-774. 3
23. Kosara R., Bendix F., Hauser H.,: Parallel Sets: Interactive Exploration and Visual Analysis of Categorical Data. IEEE TVCG 12, 4 (July 2006), 558-568. 3
24. König-Langlo G., Loose B.,: The Meteorological Observatory at Neumayer Stations (GvN and NM-II) Antarctica. Polarforschung2006 76, 1 (2007), 25-38. 8
25. Lee H., Kihm J., Choo J., Stasko J., Park H.,: iVisClustering: An Interactive Visual Document Clustering via Topic Modeling. CG Forum 31, 3pt3 (2012), 1155-1164. 3
26. Lex A., Schulz H.-J., Streit M., Partl C., Schmalstieg D.,: VisBricks: Multiform Visualization of Large, Inhomogeneous Data. IEEE Transactions on Visualization and Computer Graphics 17, 12 (2011), 2291-2300. 3
27. May T., Bannach A., Davey J., Ruppert T., Kohlhammer J.,: Guiding feature subset selection with an interactive visualization. In VAST (2011), IEEE, pp. 111-120. 3
28. Morris A.,: An information theoretic measure of sequence recognition performance. Tech. rep., 2002. 4
29. Mühlbacher T., Piringer H.,: A Partition-Based Framework for Building and Validating Regression Models. Transactions on Vis. and CG. 19, 12 (2013), 1962-1971. 3, 4
30. Manning C., Schütze H.,: Foundations of statistical natural language processing. MIT, MA, USA, 1999. 3
31. Meyer M.D., Wong B., Styczynski M.P., Munzner T., Pfister H.,: Pathline: A Tool For Comparative Functional Genomics. CG. Forum 29, 3 (2010), 1043-1052. 1
32. Novotny M., Hauser H.,: Outlier-Preserving Focus+Context Visualization in Parallel Coordinates. Visualization and Computer Graphics, IEEE 12, 5 (2006), 893-900. 3
33. Rimbu N., Lohmann G., König-Langlo G., Ionita M.,: 30 years of synoptic observations from Neumayer Station with links to datasets., 2012. 7
34. Rao J. N. K., Scott A.J.,: The Analysis of Categorical Data from Complex Sample Surveys: Chi-Squared Tests for Goodness of Fit and Independence in Two-Way Tables. Journal of the American Statistical Assoc. 76, 374 (1981), 221-230. 3
35. Schreck T., Bernard J., Tekušová T., Kohlhammer J.,: Visual Cluster Analysis of Trajectory Data With Interactive Kohonen Maps. Palgrave Macmillan Information Visualization 8 (2009), 14-29. 3
36. Sim K., Gopalkrishnan V., Zimek A., Cong G.,: A survey on enhanced subspace clustering. Data Mining and Knowledge Discovery 26, 2 (2013), 332-397. 3
37. Shimazaki H., Shinomoto S.,: A Method for Selecting the Bin Size of a Time Histogram. Neural Comput. 19, 6 (2007), 1503-1527. doi: 10.1162/neco.2007.19.6.1503. 4
38. Tominski C., Donges J.F., Nocke T.,: Information Visualization in Climate Research. In Information Visualization (Washington, DC, USA, 2011), IEEE, pp. 298-305. 1
39. Tatu A., Zhang L., Bertini E., Schreck T., Keim D., Bremm S., von Landesberger T.,: ClustNails: Visual analysis of subspace clusters. Tsinghua Science and Technology 17, 4 (2012), 419-428. 3
40. Walz J., Chun F. K.-H., Klein E.A., Reuther A., Saad F., Graefen M., Huland H., Karakiewicz P.I.,: Nomogram predicting the probability of early recurrence after radical prostatectomy for prostate cancer. The Journal of urology 181, 2 (2009), 601-608. 8
41. White R., Roth R.,: Exploratory search: Beyond the query-response paradigm. Synthesis Lectures on Information Concepts, Retrieval, and Services 1, 1 (2009), 1-98. 1
42. Yang J., Hubball D., Ward M.O., Rundensteiner E.A., Ribarsky W.,: Value and Relation Display: Interactive Visual Exploration of Large Data Sets with Hundreds of Dimensions. TVCG, IEEE 13 (2007), 494-507. 3
43. Zhou J., Konecni S., Grinstein G.,: Visually comparing multiple partitions of data with applications to clustering. Proc. SPIE 7243 (2009). doi: 10.1117/12.810093. 3