Exploration of dimensionality reduction for text visualization

Proceedings - Third International Conference on Coordinated and Multiple Views in Exploratory Visualization, CMV 2005

Volumn 2005, Issue , 2005, Pages 63-74

  Huang, Shiping ^a   Ward, Matthew O ^a   Rundensteiner, Elke A ^a  

^a Worcester Polytechnic Institute   (United States)

Author keywords

Dimension reduction; Multidimensional scaling (MDS); Self organizing maps (SOM); Text visualization

Indexed keywords

DIMENSION REDUCTION; MULTIDIMENSIONAL SCALING (MDS); SELF-ORGANIZING MAPS (SOM); TEXT VISUALIZATION;

COMPUTATION THEORY; ELECTRONIC COMMUNITIES; MATHEMATICAL MODELS; PRINCIPAL COMPONENT ANALYSIS; SELF ORGANIZING MAPS;

PROGRAM DOCUMENTATION;

EID: 33746133202     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CMV.2005.8     Document Type: Conference Paper

References (36)

1. M. Ankerst, S. Berchtold, and D. A. Keim. Similarity clustering of dimensions for an enhanced visualization of multidimensional data. Proc. of IEEE Symposium on Information Visualization, InfoVis'98, p. 52-60, 1998.
2. W. Basalaj. Proximity visualisation of abstract data. Technical Report UCAM-CL-TR-509, University of Cambridge, Computer Laboratory, 15 JJ Thomson Avenue, Cambridge CB3 0FD, United Kingdom, phone +44 1223 763500, January 2001.
3. T. Bayer, H. Mogg-Schneider, I. Renz, and H. Schafer. Daimler benz research: System and experiments routing and filtering. In Text REtrieval Conference, pages 329-346, 1997.
4. C. Buckley. Implementation of the smart information retrieval system. Technical Report TR85-686, Cornell University, Computer Science Department, may 1985.
5. M. A. Carreira-Perpinan. A review of dimension reduction techniques. Technical Report CS-96-09, Dept. of Computer Science, University of Sheffield, January 1997.
6. A. Choppin. Unsupervised classification of high dimensional data by means of self-organizing neural networks. Master's thesis, Universit catholique de Louvain (Belgium), June 1998.
7. M. Davison. Mutidimensional scaling. John Wiley & Sons, 1983.
8. P. Demartines and J. Hérault. Curvilinear component analysis: A self-organizing neural network for nonlinear mapping of data sets. IEEE Transactions on Neural Networks, 8(1):148-154, January 1997.
9. C. Ding, X. He, H. Zha, and H. Simon. Adaptive dimension reduction for clustering high dimensional data. In Proc. 2nd IEEE Int'l Conf. Data Mining, pages 147-154, December 2002.
10. C. H. Ding. A probabilistic model for dimensionality reduction in information retrieval and filtering. In Proc. of 1st SIAM Computational Information Retrieval Workshop, October 2000.
11. A. Flexer. On the use of self-organizing maps for clustering and visualization. Intelligent-Data-Analysis, 5:373-84, 2001.
12. W. B. Frakes. Stemming algorithms. In W. B. Frakes and R. Baeza-Yates, editors, Information Retrieval: data structures and algorithms, pages 131-160. Prentice Hall, Englewood Cliffs, US, 1992.
13. A. Globerson and N. Tishby. Sufficient dimensionality reduction - a novel analysis method. In ICML, pages 203-210. IEEE Service Center, July 2002.
14. S. Havre, E. Hetzler, P. Whitney, and L. Nowell. ThemeRiver: Visualizing thematic changes in large document collections. IEEE Transactions on Visualization and Computer Graphics, 8(1):9-20, January 2002.
15. T. Honkela. Comparisons of self-organized word category maps. In Proceedings of WSOM'97, Workshop on Self-Organizing Maps, Espoo, Finland, June 4-6, pages 298-303. Helsinki University of Technology, Neural Networks Research Centre, Espoo, Finland, 1997.
16. T. Honkela, S. Kaski, K. Lagus, and T. Kohonen. Exploration of full-text databases with self-organizing maps. In Proceedings of the ICNN96, International Conference on Neural Networks, volume 1, pages 56-61. IEEE Service Center, Piscataway, NJ, 1996.
17. T. Honkela, S. Kaski, K. Lagus, and T. Kohonen. WEBSOM-self-organizing maps of document collections. In Proceedings of WSOM'97, Workshop on Self-Organizing Maps, Espoo, Finland, June 4-6, pages 310-315. Helsinki University of Technology, Neural Networks Research Centre, Espoo, Finland, 1997.
18. J. Jolliffe. Principal Component Analysis. Springer Verlag, 1986.
19. G. Karypis and E.-H. Han. Concept indexing: A fast dimensionality reduction algorithm with applications to document retrieval and categorization. Technical report tr-00-0016, University of Minnesota, 2000.
20. S. Kaski. Dimensionality reduction by random mapping: Fast similarity computation for clustering. In Proceedings of IJCNN'98, International Joint Conference on Neural Networks, volume 1, pages 413-418. IEEE Service Center, Piscataway, NJ, 1998.
21. T. Kohonen. The self-organizing map. Proc. of IEEE, p. 1464-80, 1978.
22. T. Kohonen. Self Organizing Maps. Springer Verlag, 1995.
23. T. Kohonen, S. Kaski, K. Lagus, and T. Honkela. Very large two-level SOM for the browsing of newsgroups. In C. von der Malsburg, W. von Seelen, J. C. Vorbrüggen, and B. Sendhoff, editors, Proceedings of ICANN96, International Conference on Artificial Neural Networks, Bochum, Germany, July 16-19, 1996, Lecture Notes in Computer Science, vol. 1112, pages 269-274. Springer, Berlin, 1996.
24. X. Lin, D. Soergel, and G. Marchionini. A self-organizing semantic map for information retrieval. In Proceedings of the Fourteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Semantic Models, pages 262-269, 1991.
25. A. Martin and M. Ward. High dimensional brushing for interactive exploration of multivariate data. Proc. of Visualization'95, p. 271-8, 1995.
26. A. K. McCallum. Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering. http://www.cs.cmu.edu/mccallum/bow, 1996.
27. H. Ritter and T. Kohonen. Self-organizing semantic maps. Biological Cybernetics, 61(4):241-54, 1989.
28. J. Thomas, K. Cook, V. Crow, B. Hetzler, R. May, D. McQuerry, R. McVeety, N. Miller, G. Nakamura, L. Nowell, P. Whitney, and P. Wong. Human computer interaction with global information spaces - beyond data mining, 1999.
29. M. Ward. Xmdvtool: Integrating multiple methods for visualizing multivariate data. Proc. of Visualization '94, p. 326-33, 1994.
30. M. Ward, J. LeBlanc, and R. Tipnis. N-land: a graphical tool for exploring n-dimensional data. CG194 Proc: Insight Through Computer Graphics, p. 130-41, 1996.
31. R. W. White, I. Ruthven, and J. M. Jose. Finding relevant documents using top ranking sentences: an evaluation of two alternative schemes. In M. Beaulieu, R. Baeza-Yates, S. H. Myaeng, and K. Järvelin, editors, Proceedings of the 25th annual international ACM SIGIR Conference on Research and Development in Information Retrieval, pages 57-64, New York, Aug. 11-15 2002. ACM Press.
32. J. A. Wise. The ecological approach to text visualization. JASIS, Vol. 50, No. 13, p. 1224-1233, 1999.
33. J. A. Wise, J. J. Thomas, K. Pennock, D. Lantrip, M. Pottier, A. Schur, and V. Crow. Visualizing the non-visual: Spatial analysis and interaction with information from text documents. Proc. of Information Visualization '1995, p. 51-58, 1995.
34. J. Yang, M. O. Ward, and E. A. Rundensteiner. Interactive hierarchical displays: A general framework for visualization and exploration of large multivariate data sets. Computer & Graphics, to appear, 2002.
35. J. Yang, M. O. Ward, and E. A. Rundensteiner. Interring: An interactive tool for visually navigating and manipulating hierarchical structures. IEEE Symposium on Information Visualization (InfoVis'02), p. 77-84, 2002.
36. J. Yang, M. O. Ward, E. A. Rundensteiner, and S. Huang. Visual hierarchical dimension reduction for exploration of high dimensional datasets. VisSym 2003, accepted, 2003.