Feedback-driven interactive exploration of large multidimensional data supported by visual classifier

2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings

Volumn , Issue , 2015, Pages 43-52

  Behrisch, Michael ^a   Korkmaz, Fatih ^a   Shao, Lin ^a   Schreck, Tobias ^a  

^a UNIVERSITY OF KONSTANZ   (Germany)

Author keywords

Interesting View Problem; Relevance Feedback; User Preference Model; View Space Exploration Framework

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; DECISION SUPPORT SYSTEMS; GRAPHICAL USER INTERFACES; SEMANTICS; SPACE RESEARCH; VISUALIZATION;

CONVERGENCE AND STABILITY; HIGH DIMENSIONAL DATA; INTERACTIVE EXPLORATION; INTERESTING VIEW PROBLEM; MULTIDIMENSIONAL DATA; RELEVANCE FEEDBACK; SPACE EXPLORATIONS; USER PREFERENCE MODELING;

FEEDBACK;

EID: 84929454424     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/VAST.2014.7042480     Document Type: Conference Paper

References (37)

1. K. Bache and M. Lichman. University of California (UCI) machine learning repository. Online, June 2013. http://archive.ics.uci.edu/ml/.
2. R. A. Baeza-Yates and B. A. Ribeiro-Neto. Modern Information Retrieval\-The concepts and technology behind search, Second edition. Pearson Education Ltd., Harlow, England, 2011.
3. P. Baudisch, N. Good, and P. Stewart. Focus plus context screens: Combining display technology with visualization techniques. In Proceedings of the 14th Annual ACM Symposium on User Interface Software and Technology, UIST '01, pages 31-40, New York, NY, USA, 2001. ACM.
4. E. Brown, J. Liu, C. Brodley, and R. Chang. Dis-function: Learning distance functions interactively. In Visual Analytics Science and Technology (VAST), 2012 IEEE Conference on, pages 83-92, Oct 2012.
5. S. K. Card, J. D. Mackinlay, and B. Shneiderman. Readings in information visualization: using vision to think. Morgan Kaufmann, 1999.
6. G. Cauwenberghs and T. Poggio. Incremental and decremental support vector machine learning. Advances in neural information processing systems, pages 409-415, 2001.
7. W. S. Cleveland. Visualizing Data. Hobart Press, 1993.
8. T. N. Dang and L. Wilkinson. ScagExplorer: Exploring Scatterplots by Their Scagnostics. In Pacific Visualization Symposium (PacificVis), 2014 IEEE, pages 73-80, March 2014.
9. A. Dasgupta and R. Kosara. Pargnostics: Screen-space metrics for parallel coordinates. Visualization and Computer Graphics, IEEE Transactions on, 16(6):1017-1026, Nov 2010.
10. T. Deselaers, D. Keysers, and H. Ney. Features for image retrieval: an experimental comparison. Information Retrieval, 11(2):77-107, Apr. 2008.
11. R. O. Duda and P. E. Hart. Use of the hough transformation to detect lines and curves in pictures. Commun. ACM, 15(1):11-15, Jan. 1972.
12. A. Endert, P. Fiaux, and C. North. Semantic interaction for visual text analytics. In Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems, CHI '12, page 473-482, New York, NY, USA, 2012. ACM, ACM.
13. J. Friedman and J. Tukey. A projection pursuit algorithm for exploratory data analysis. Computers, IEEE Transactions on, C-23(9):881-890, Sept 1974.
14. J. Han, M. Kamber, and J. Pei. Data Mining: Concepts and Techniques. Elsevier Ltd, Oxford, 3rd edition, 2011.
15. J. Han, M. Kamber, and J. Pei. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 3rd edition, 2011.
16. C. G. Healey and B. M. Dennis. Interest driven navigation in visualization. IEEE Trans. Vis. Comput. Graph., 18(10):1744-1756, 2012.
17. F. Heimerl, S. Koch, H. Bosch, and T. Ertl. Visual classifier training for text document retrieval. IEEE Transactions on Visualization and Computer Graphics (TVCG), 18(12):2839-2848, 2012.
18. J. Heinrich, J. Stasko, and D. Weiskopf. The parallel coordinates matrix. EuroVis-Short Papers, pages 37-41, 2012.
19. M. Hess, S. Bremm, S. Weissgraeber, K. Hamacher, M. Goesele, J. Wiemeyer, and T. von Landesberger. Visual exploration of parameter influence on phylogenetic trees. IEEE Computer Graphics and Applications, 99(PrePrints):1, 2014.
20. J. Hope. Chocolate and red wine can help stave off diabetes: High levels of antioxidants can regulate blood glucose levels. Online, January 2014. accessed 29 March 2014.
21. J.-F. Im, M. McGuffin, and R. Leung. Gplom: The generalized plot matrix for visualizing multidimensional multivariate data. Visualization and Computer Graphics, IEEE Trans. on, 19(12):2606-2614, Dec 2013.
22. D. A. Keim, D. Morent, J. Schneidewind, M. C. Hao, and U. Dayal. Intelligent Visual Analytics Queries. In Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST 2007), 2007.
23. C. D. Manning, P. Raghavan, and H. Schutze. Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA, 2008.
24. McKinsey Global Institute. Big data: The next frontier for innovation, competition, and productivity. Online, May 2011.
25. C. Pich. Mdsj: Java library for multidimensional scaling (version 0.2). online, 2009. Available at http://www.inf.unikonstanz. de/algo/software/mdsj/.
26. Y. Rui, T. S. Huang, M. Ortega, and S. Mehrotra. Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans. Circuits Syst. Video Techn., 8(5):644-655, 1998.
27. S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Pearson Education, 2 edition, 2003.
28. B. Shneiderman. The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings of the 1996 IEEE Symposium on Visual Languages, VL '96, pages 336-, Washington, DC, USA, 1996. IEEE Computer Society.
29. M. Sips, B. Neubert, J. Lewis, and P. Hanrahan. Selecting good views of high-dimensional data using class consistency. Computer Graphics Forum, 28(3):831-838, 2009.
30. D. Tao, X. Tang, X. Li, and X. Wu. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence, 28(7):1088-1099, 2006.
31. A. Tatu, G. Albuquerque, M. Eisemann, P. Bak, H. Theisel, M. A. Magnor, and D. A. Keim. Automated analytical methods to support visual exploration of high-dimensional data. IEEE Trans. Vis. Comput. Graph., 17(5):584-597, 2011.
32. A. Tatu, F. Maass, I. Faerber, E. Bertini, T. Schreck, T. Seidl, and D. A. Keim. Subspace Search and Visualization to Make Sense of Alternative Clusterings in High-Dimensional Data. In Procedings of IEEE VAST, pages 63-72. IEEE CS Press, 2012.
33. M. O.Ward and Z. Guo. Visual exploration of time-series data with shape space projections. Comput. Graph. Forum, 30(3):701-710, 2011.
34. L. Wilkinson, A. Anand, and R. Grossman. Graph-theoretic scagnostics. In Information Visualization, 2005. INFOVIS 2005. IEEE Symposium on. IEEE Computer Society, 2005.
35. H. Yang and S. Fong. Incrementally optimized decision tree for noisy big data. In Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, BigMine '12, pages 36-44, New York, NY, USA, 2012. ACM.
36. J. Yang, D. Hubball, M. O. Ward, E. A. Rundensteiner, and W. Ribarsky. Value and relation display: Interactive visual exploration of large data sets with hundreds of dimensions. IEEE Trans. Vis. Comput. Graph., 13(3):494-507, 2007.
37. B. Yegnanarayana. Artificial neural networks. PHI Learning Pvt. Ltd., 2009.