Visual human+machine learning

IEEE Transactions on Visualization and Computer Graphics

Volumn 15, Issue 6, 2009, Pages 1317-1325

  Fuchs, Raphael ^a   Waser, Jürgen ^b   Gröller, Meister Eduard ^c  

^a ETH ZURICH   (Switzerland)

^b VRVIS RESEARCH CENTER   (Austria)

^c VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Computerassisted Multivariate Data Exploration; Curse of Dimensionality; Genetic Algorithm; Interactive Visual Analysis; Knowledge Discovery; Multiple Competing Hypotheses; Predictive Analysis; Volumetric Data

Indexed keywords

DATA MINING; FUZZY LOGIC; HEURISTIC ALGORITHMS; KNOWLEDGE BASED SYSTEMS; LEARNING ALGORITHMS; MACHINE LEARNING; PATTERN RECOGNITION; PETROLEUM RESERVOIR EVALUATION; PREDICTIVE ANALYTICS; VOLUME RENDERING; VOLUMETRIC ANALYSIS;

CURSE OF DIMENSIONALITY; INTERACTIVE VISUAL ANALYSIS; MULTIPLE COMPETING HYPOTHESES; MULTIVARIATE DATA; VOLUMETRIC DATA;

GENETIC ALGORITHMS;

EID: 77957924524     PISSN: 10772626     EISSN: None     Source Type: Journal    
DOI: 10.1109/TVCG.2009.199     Document Type: Conference Paper

References (31)

1. M. Ankerst. Visual Data Mining. PhD thesis, Faculty of Mathematics and Computer Science, University of Munich, 2000.
2. D. Asimov. The grand tour: a tool for viewing multidimensional data. SIAM J. Sci. Stat. Comput., 6(1):128-143, 1985.
3. R. Bürger, P. Muigg, M. Ilc̀k, H. Doleisch, and H. Hauser. Integrating local feature detectors in the interactive visual analysis of flow simulation data. In Proc. EuroVis 2007, pages 171-178, 2007.
4. D. Caragea, D. Cook, H. Wickhamand, and V. Honavar. Visual methods for examining SVM classifiers. Visual Data Mining: Theory, Techniques and Tools for Visual Analytics, pages 136-153, 2008.
5. C. Chen. Top 10 unsolved information visualization problems. IEEE Comput. Graph. Appl., 25(4):12-16, 2005.
6. H. Doleisch, M. Gasser, and H. Hauser. Interactive feature specification for focus+context visualization of complex simulation data. In Proc. Vis-Sym 2003, pages 239-248, 2003.
7. T. M. English. Optimization is easy and learning is hard in the typical function. In Proc. Congress on Evolutionary Computation: CEC00, pages 924-931, 2000.
8. M. Hao, U. Dayal, D. Keim, D. Morent, and J. Schneidewind. Intelligent visual analytics queries. In Proc. VAST 2007., pages 91-98, 2007.
9. H. Hauser, R. Laramee, and H. Doleisch. Topology-based versus feature-based flow analysis-challenges and an application. In Proceedings of TopoInVis 2007: Topology-Based Methods in Visualization, 2007.
10. A. Hertzmann. Machine learning for computer graphics: A manifesto and tutorial. In Proc. Pacific Graphics 2003, page 22. IEEE Computer Society, 2003.
11. H. Jänicke, A. Wiebel, G. Scheuermann, and W. Kollmann. Multifield visualization using local statistical complexity. IEEE Transactions on Visualization and Computer Graphics, 13(5):168-175, 2007.
12. M. Jeng. A selected history of expectation bias in physics. American Journal of Physics, 74:578-583, 2006.
13. J. Kehrer, F. Ladstadter, P. Muigg, H. Doleisch, A. Steiner, and H. Hauser. Hypothesis generation in climate research with interactive visual data exploration. IEEE Transactions on Visualization and Computer Graphics, 14(6):1579-1586, Nov.-Dec. 2008.
14. D. Keim, F. Mansmann, J. Schneidewind, J. Thomas, and H. Ziegler. Visual analytics: Scope and challenges. Lecture Notes In Computer Science, pages 76-90, 2008.
15. D. A. Keim. Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics, 8(1):1-8, 2002.
16. S. Laine. Using visualization, variable selection and feature extraction to learn from industrial data. PhD thesis, TU Helsinki, 2003.
17. R. Laramee and H. Hauser. Geometric flow visualization techniques for cfd simulation data. In Proceedings of the 21st spring conference on Computer graphics, 2005.
18. K.-L. Ma. Machine learning to boost the next generation of visualization technology. IEEE Comput. Graph. Appl., 27(5):6-9, 2007.
19. T. May and J. Kohlhammer. Towards closing the analysis gap: Visual generation of decision supporting schemes from raw data. Computer Graphics Forum, 27(3):911-918, 2008.
20. W. Müller, T. Nocke, and H. Schumann. Enhancing the visualization process with principal component analysis to support the exploration of trends. In Asia Pacific Symposium on Information Visualisation, volume 60, pages 121-130, 2006.
21. Nvidia's CUDA. www.nvidia.com/object/cuda-home.html.
22. F. Poulet. SVM and graphical algorithms: a cooperative approach. In ICDM '04. Fourth IEEE International Conference on Data Mining, 2004., pages 499-502, 2004.
23. F. Poulet. Visual Data Mining, chapter Towards Effective Visual Data Mining with Cooperative Approaches, pages 389-406. Springer, 2008.
24. P. Rautek. Semantic Visualization Mapping for Volume Illustration. PhD thesis, Vienna University of Technology, 2009.
25. F. Rossi. Visual data mining and machine learning. In European Symposium on Artificial Neural Networks (ESANN2006), 2006.
26. J. Seo and B. Shneiderman. A rank-by-feature framework for interactive exploration of multidimensional data. Information Visualization, 4(2):96-113, 2005.
27. B. Shneiderman. Inventing discovery tools: combining information visualization with data mining. Information Visualization, 1(1):5-12, 2002.
28. F.-Y. Tzeng, E. B. Lum, and K.-L. Ma. An intelligent system approach to higher-dimensional classification of volume data. IEEE Transactions on Visualization and Computer Graphics, 11(3):273-284, 2005.
29. F.-Y. Tzeng and K.-L. Ma. Intelligent feature extraction and tracking for visualizing large-scale 4d flow simulations. In Proc. of the 2005 ACM/IEEE conference on Supercomputing, page 6, 2005.
30. M. Ward. Finding needles in large-scale multivariate data haystacks. IEEE Comput. Graph. Appl., 24(5):16-19, 2004.
31. D. Yang, E. A. Rundensteiner, and M. O. Ward. Analysis guided visual exploration of multivariate data. In Visual Analytics Science and Technology, VAST 2007., pages 83-90, 2007.