Visualization methods for metric studies

S. K. Card, J. D. Mackinlay, and B. Shneiderman, editors. Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann, San Francisco, 1999.

C. G. Healey, K. S. Booth, and J.T. Enns. Visualizing real-time multivariate data using preattentive processing. ACM Transactions on Modeling and Computer Simulation, 5(3):190-221, July 1995.

J.-D. Fekete and C. Plaisant. Interactive information visualization of a million items. In Proceedings of IEEE Symposium on Information Visualization 2002 (InfoVis 2002), pages 117-124, Boston (USA), 2002.

J. D. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions on Graphics, 5(2):110-141, April 1986.

P. E. Hoffman. Table Visualizations: A Formal Model and Its Applications. PhD thesis, University of Massachusetts at Lowell, 1999.

C. J. C. Burges. Geometric methods for feature extraction and dimensional reduction. In L. Rokach and O. Maimon, editors, Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers. Kluwer Academic Publishers, 2005.

G. Salton, C. Yang, and A. Wong. A vector space model for automatic indexing. Communications of the ACM, 18(11):613-620, 1975.

H. D. White, and K. W. McCain. Visualization of literatures. In M. E. Williams, editor, Annual Review of Information Science and Technology, 32:99-168. Medford, NJ: Information Today, 1997.

K. Börner, C. Chen, and K. Boyack. Visualizing Knowledge Domains. In B. Cronin, editor, Annual Review of Information Science & Technology, 37:179-255. Medford, NJ: Information Today, 2003.

J.-D. Fekete, G. Grinstein, and C., Plaisant. IEEE InfoVis 2004 Contest, the history of InfoVis, http://www.cs.umd.edu/hcil/iv04contest/, 2004.

J.-D. Fekete and C. Plaisant. Les leçons tirées des deux compétitions de visualisation d'information. In Proceedings of IHM2004, pages 7-12, Namur, Belgium, September 2004.

W. S. Torgerson. Multidimensional scaling: I. theory and method. Psychometrika, 17:401-419, 1952.

J. B. Kruskal. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29:1--27, 1964.

J. B. Kruskal. Nonmetric multidimensional scaling: a numerical method. Psychometrika, 29:115--129, 1964.

J. W. Sammon. A nonlinear mapping for data structure analysis. IEEE Transactions on Computer, C-18(5):401-409, May 1969.

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, 1997.

A. Morrison, G., Ross, and M. Chalmers. Multidimensional Scaling through Sampling, Springs and Interpolation, Information Visualization 2(1): 68-77, March 2003.

A. Morrison, and M. Chalmers. A Pivot-Based Routine for Improved Parent-Finding in Hybrid MDS,

Information Visualization 3(2):109-112, 2004.

M. M. Bronstein, A. M. Bronstein, R. Kimmel, and I. Yavneh, Multigrid multidimensional scaling, Numerical Linear Algebra with Applications, 13(2-3):149-171, 2006.

J. Laub, and K.-R. Müller. Feature Discovery in Non-Metric Pairwise Data, Journal of Machine Learning Research, 5:801-818, July 2004.

G. S. Davidson, B. Hendrickson, D. K. Johnson, C. E. Meyers, and B. N. Wylie. Knowledge Mining With VxInsight: Discovery Through Interaction, Journal of Intelligent Information Systems, 11:259-285, 1998.

G. S. Davidson, B. N. Wylie, and K. W. Boyack. Cluster stability and the use of noise in interpretation of clustering. Proc. IEEE Information Visualization, pages 23-30, 2001.

C. Faloutsos and K.-I. Lin. FastMap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In Proceedings ACM SIGMOD'95, pages 163-174, 1995.

J. C. Platt. FastMap, MetricMap, and Landmark MDS are all Nystörm algorithms. In Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, pages 261-268, 2005.

J. Venna and S. Kaski. Neighborhood preservation in nonlinear projection methods: An experimental study. In Proceedings of ICANN 2001, pages 485-491, Berlin, 2001.

S. Kaski, J. Nikkila, M. Oja, J. Venna, P. Toronen, and E. Castren. Trustworthiness and metrics in visualizing similarity of gene expression. BMC Bioinformatics:4, 2003.

M. Aupetit. Visualizing distortion in continuous projection techniques. In Proceedings of XIIth European Symposium on Artificial Neural Networks (ESANN 2004), pages 465-470, Bruges (Belgium), April 2004.

R. C. T. Lee, J. R. Slagle, and H. Blum. A triangulation method for the sequential mapping of points from N-Space to Two-Space. IEEE Transactions on Computer (26):288-292, 1977.

T. Kohonen. Self-Organizing Maps, volume~30 of Springer Series in Information Sciences. Springer, third edition, 2001.

S. A. Morris, C. DeYong, Z. Wu, S. Salman, and D. Yemenu. DIVA: a visualization system for exploring document databases for technology forecasting. Computers & Industrial Engineering 43:841-862, 2002.

X. Lin, H. D. White, and J. Buzydlowski. Real-time author co-citation mapping for online searching. International Journal of Information Processing & Management, 39(5):689-706, 2003.

C. Ambroise and G. Govaert. Analyzing dissimilarity matrices via Kohonen maps. In Proceedings of 5th Conference of the IFCS 1996, volume 2, pages 96-99, Kobe (Japan), 1996.

T. Kohonen and P. J. Somervuo. Self-organizing maps of symbol strings. Neurocomputing, 21:19-30, 1998.

T. Graepel and K. Obermayer. A stochastic self-organizing map for proximity data. Neural Computation, 11(1):139-155, 1999.

A. El Golli, B. Conan-Guez, and F. Rossi. A self organizing map for dissimilarity data. In Proceedings of IFCS 2004, pages 61-68, Chicago, Illinois (USA), 2004.

B. Conan-Guez, F. Rossi, and A. El Golli. Fast algorithm and implementation of dissimilarity self-organizing maps. Neural Networks, to be published in 2006.

S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. A. Harshman. Indexing by latent semantic analysis, Journal of the American Society for Information Science, 41(6):391-407, 1990.

X. Lin, D. Soergel, and G. Marchionini. A self-organizing semantic map for information retrieval. Proceedings of the Fourteenth Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, pages 262-269, 1991.

T. Kohonen, S. Kaski, K. Lagus, J. Salöjarvi, J. Honkela, V. Paatero, and A. Saarela. Self organization of a massive text document collection. IEEE Transactions on Neural Networks, 11(3):574-585, May 2000.

J. Vesanto. SOM-based data visualization methods. Intelligent Data Analysis, 3(2):111--126, 1999.

J. Vesanto. Data Exploration Process Based on the Self-Organizing Map. PhD thesis, Helsinki University of Technology, Espoo (Finland), May 2002.

J. Himberg. From Insights to Innovations: Data Mining, Visualization, and User Interfaces. PhD thesis, Helsinki University of Technology, Espoo (Finland), November 2004.

H. Chen, C. Schuffels, and R. Orwig. Internet Categorization and Search: A Self-Organizing Approach. Journal of Visual Communication and Image Representation, Special Issue on Digital Libraries, 7(1):88-102, 1996.

D. Roussinov, and H. Chen. A Scalable Self-Organizing Map Algorithm for Textual Classification: A Neural Network Approach to Automatic Thesaurus Generation. Communication and Cognition in Artificial Intelligence Journal (CC-AI), 15(1-2):81-111, 1998.

T. Ong, H. Chen, W. Sung, and B. Zhu. Newsmap: a knowledge map for online news. Decision Support Systems, 39:583-597, 2005.

R. T. Freeman, and H. Yin. Tree view self-organization of web content. Neurocomputing, 63:415-446, 2005.

J.-C. Lamirel. Application d'une approche symbolico-connexionniste pour la conception d'un système documentaire hautement interactif, Thèse de l'Université de Nancy 1 Henri Poincaré, 1995.

X. Polanco, C. Francois, and J. C. Lamirel. Using artificial neural networks for mapping of science and technology: A multi-self-organizing-maps approach. Scientometrics, 51(1): 267-292, 2001.

49. J. Lamping, R. Rao, and P. Pirolli. A focus+context technique based on hyperbolic geometry for visualizing large hierarchies. In Proceedings of the ACM Conference on Human Factors in Computing Systems, CHI, pages 401-408, 1995.

H. Ritter. Self-organizing maps in non-euclidean spaces. In E. Oja and S. Kaski, editors, Kohonen Maps, pages 97-108. Elsevier, Amsterdam, 1999.

J. Ontrup and H. Ritter. A hierarchically growing hyperbolic self-organizing map for rapid structuring of large data sets. In Proceedings of the 5th Workshop on Self-Organizing Maps, Paris (France), 2005.

C. Yang, H. Chen and K. Hong. Visualization of large category map for Internet browsing. Decision Support Systems, 35(1):89-102, April 2003.

A. Skupin, and B. P. Buttenfield. Spatial Metaphors for Visualizing Very Large Data Archives. Proceedings GIS/LIS’96. Bethesda: American Society for Photogrammetry and Remote Sensing, pages 607-617, 1996.

A. Skupin. From Metaphor to Method: Cartographic Perspectives on Information Visualization. In: Roth, S.F., and Keim, D.A. (Eds.) Proceedings IEEE Symposium on Information Visualization (InfoVis 2000), Salt Lake City, Utah, pages 91-97, 2000.

A. Skupin. A Cartographic Approach to Visualizing Conference Abstracts. IEEE Computer Graphics and Applications, 22(1):50-58, 2002.

A. Skupin, and S. I. Fabrikant. Spatialization Methods: A Cartographic Research Agenda for Non-Geographic Information Visualization. Cartography and Geographic Information Science, 30(2):99-119, 2003.

C. M. Bishop. Latent variable models. In M. I. Jordan editor, Learning in Graphical Models, pp. 371–403. MIT Press, 1999.

J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(22):2319-2323, December 2000.

J. A. Lee, A. Lendasse, and M. Verleysen. Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis. Neurocomputing, 57:49-76, March 2004.

S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(22):2323-2326, December 2000.

L. K. Saul, K. Q. Weinberger, F. Sha, J. Ham, and D. D. Lee. Spectral methods for dimensionality reduction. In B. Schlkopf, O. Chapelle, and A. Zien, editors, Semisupervised Learning. MIT Press, Cambridge, MA, 2006.

M. E. Tipping and C. M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B, 61(3):611-622, 1999.

M. E. Tipping and C. M. Bishop. Mixtures of probabilistic principal component analyzers. Neural Computation, 11(2):443-482, 1999.

C. M. Bishop and M. E. Tipping. A hierarchical latent variable model for data visualization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3):281-293, 1998.

C. M. Bishop, M. Svensén, and C. K. I. Williams. GTM: The generative topographic mapping. Neural Computation, 10(1):215-34, 1998.

P. Tino and I. Nabney. Hierarchical GTM: Constructing localized non-linear projection manifolds in a principled way. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):639-656, 2002.

C. M. Bishop, M. Svensén, and C. K. I. Williams. Developments of the generative topographic mapping. Neurocomputing, 21:203-224, 1998.

A. Kabán and M. Girolami. Combined Latent Class and Trait Model for the Analysis and Visualisation of Discrete Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(8):859-872, 2001.

I. Nabney, Y. Sun, P. Tino, and A Kabán. Semisupervised Learning of Hierarchical Latent Trait Models for Data Visualization. IEEE Transactions on Knowledge and Data Engineering, 17(3), 2005.

A. Kabán and M. Girolami. A Dynamic Probabilistic Model to Visualize Topic Evolution in Text Streams. Journal of Intelligent Information Systems, special issue on Automated Text Categorization, 18(2/3):107-125, 2002.

P. Tino, A. Kabán, and Y. Sun. A Generative Probabilistic Approach to Visualizing Sets of Symbolic Sequences. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - (KDD04), August 22-25, Seattle, Washington, USA, pages 701-706, 2004.

M. E. J. Newman. The structure and function of complex networks. SIAM Review, 45:167-256, 2003.

E. Garfield, I. H. Sher, and R. J. Torpie. The Use of Citation Data in Writing the History of Science. Published by The Institute for Scientific Information, December 1964.

H. Small, and E. Garfield. The geography of science: disciplinary and national mappings. Journal of Information Science, 11(4) :147-159, 1985.

C. Chen. Structuring and visualizing the WWW with Generalized Similarity Analysis. In 8th ACM Conference on Hypertext (Hypertext '97), Southampton, UK, ACM Press, pages 177-186, 1997.

C. Chen. Generalized Similarity Analysis and Pathfinder Network Scaling. Interacting with Computers, 10 (2):107-128, 1998.

R. W. Schvaneveldt. Pathfinder Associative Networks: Studies in Knowledge Organization. Norwood, NJ. Ablex Publishing, 1990.

T. Kamada, and S. Kawai. An algorithm for drawing general undirected graphs. Information Processing Letters, 31(1):7-15, 1989.

C. Chen. Visualizing semantic spaces and author co-citation networks in digital libraries. Information Processing and Management, 35(2):401-420, 1999.

S. Noel, C.-H. H. Chu, and V. Raghavan. Co-Citation Count versus Correlation for Influence Network Visualization, Information Visualization, 2(3), 2003.

C. Chen, and S. Morris. Visualizing evolving networks: Minimum spanning trees versus Pathfinder networks. In IEEE Symposium on Information Visualization, Seattle, Washington, IEEE Computer Society Press, pages 67-74, 2003.

G. Di Battista, P. Eades, R. Tamassia, and I. G. Tollis. Graph Drawing: Algorithms for the Visualization of Graphs. Prentice Hall, 1999.

I. Herman, G. Melançon, and M. Scott Marshall. Graph visualization and navigation in information visualization. IEEE Transactions on Visualization and Computer Graphics, 6(1):24--43, 2000.

V. Batagelj, and A. Mrvar. Pajek: Program Package for large network analysis (http://vlado.fmf.uni-lj.si/pub/networks/pajek/). XVII Sunbelt Social Networks Conference San Diego, February 13-16, 1997.

W. Ke, K. Börner, and L. Viswanath. Major Information Visualization Authors, Papers and Topics in the ACM Library. IEEE Symposium on Information Visualization (INFOVIS'04), 2004.

A. Ahmed, T. Dwyer, C. Murray, L. Song, Y. X. Wu. WilmaScope Graph Visualisation. IEEE Symposium on Information Visualization (INFOVIS'04), 2004.

T. Dwyer and P. Eckersley. WilmaScope - a 3D Graph Visualization System. In Graph Drawing Software, M. Junger and P. Mutzel, editors, series "Mathematics and Visualization", Springer Verlag, 2003.

M. Delest, T. Munzner, D. Auber, J.-P. Domenger. Exploring InfoVis Publication History with Tulip. IEEE Symposium on Information Visualization (INFOVIS'04), 2004.

D. Auber, Y. Chiricota, F. Jourdan, and G. Melançon. Multiscale Visualization of Small World Networks, IEEE Symposium on Information Visualization (INFOVIS'03), pages 75-81, 2003.