A discussion on visual interactive data exploration using self-organizing maps

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6731 LNCS, Issue , 2011, Pages 178-187

  Moehrmann, Julia ^a   Burkovski, Andre ^a   Baranovskiy, Evgeny ^a   Heinze, Geoffrey Alexeij ^a   Rapoport, Andrej ^a   Heidemann, Gunther ^a  

^a UNIVERSITY OF STUTTGART   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DATA EXPLORATION; DATA SETS; INTERACTIVE DATA EXPLORATION; VISUAL DATA EXPLORATION; VISUALIZATION TECHNIQUE; DATA SET; SELF ORGANIZING MAPS(SOMS); STATE OF THE ART;

COMPUTER SOFTWARE; CONFORMAL MAPPING; DATA VISUALIZATION; USER INTERFACES; VISUALIZATION;

SELF ORGANIZING MAPS;

EID: 79959315776     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-21566-7_18     Document Type: Conference Paper

References (30)

1. Kohonen, T.: The Self-Organizing Map. Proceedings of the IEEE 78(9), 1464-1480 (1990).
2. Ultsch, A., Siemon, H.P.: Kohonen's Self Organizing Feature Maps for Exploratory Data Analysis. In: International Neural Networks Conference, pp. 305-308. Kluwer Academic Press, Paris (1990).
3. Ultsch, A.: Maps for the Visualization of High-Dimensional Data Spaces. In:Workshop on Self-Organizing Maps, pp. 225-230 (2003).
4. Ultsch, A.: U*-Matrix: A Tool to Visualize Clusters in High Dimensional Data. Technical Report 36, Dept. of Mathematics and Computer Science, University of Marburg, Germany (2003).
5. Merkl, D., Rauber, A.: Alternative Ways for Cluster Visualization in Self-Organizing Maps. In: Workshop on Self-Organizing Maps, pp. 106-111 (1997).
6. Pampalk, E., Rauber, A., Merkl, D.: Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 871-876. Springer, Heidelberg (2002).
7. P̈olzlbauer, G., Rauber, A., Dittenbach, M.: Advanced visualization techniques for self-organizing maps with graph-based methods. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 75-80. Springer, Heidelberg (2005). (Pubitemid 41314113)
8. Poelzlbauer, G., Dittenbach, M., Rauber, A.: A Visualization Technique for Self-Organizing Maps with Vector Fields to Obtain the Cluster Structure at Desired Levels of Detail. IEEE International Joint Conference on Neural Networks 3, 1558-1563 (2005). (Pubitemid 44591434)
9. Poelzlbauer, G., Dittenbach, M., Rauber, A.: Advanced Visualization of Self-Organizing Maps with Vector Fields. Neural Networks 19(6-7), 911-922 (2006). (Pubitemid 44148950)
10. Tasdemir, K., Merenyi, E.: Exploiting Data Topology in Visualization and Clustering of Self-Organizing Maps. IEEE Transactions on Neural Networks 20(4), 549-562 (2009).
11. Latif, K., Mayer, R.: Sky-Metaphor Visualisation for Self-Organising Maps. J. Universal Computer Science (7th International Conference on Knowledge Management), 400-407 (2007).
12. Vesanto, J.: SOM-based Data Visualization Methods. Intelligent Data Analysis 3, 111-126 (1999). (Pubitemid 33260739)
13. Mayer, R., Aziz, T.A., Rauber, A.: Visualising Class Distribution on Self-Organising Maps. In: de Śa, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4669, pp. 359-368. Springer, Heidelberg (2007).
14. Rauber, A.: LabelSOM: on the Labeling of Self-Organizing Maps. In: International Joint Conference on Neural Networks, vol. 5, pp. 3527-3532 (1999). (Pubitemid 30593457)
15. Neumayer, R., Mayer, R., Polzlbauer, G., Rauber, A.: The Metro Visualisation of Component Planes for Self-Organising Maps. In: International Joint Conference on Neural Networks, pp. 2788-2793 (2007).
16. Dzemyda, G., Kurasova, O.: Comparative Analysis of the Graphical Result Presentation in the SOM Software. Informatica 13(3), 275-286 (2002).
17. Keim, D., Mansmann, F., Schneidewind, J., Ziegler, H.: Challenges in Visual Data Analysis. In: 10th International Conference on Information Visualization, pp. 9-16 (2006).
18. Heidemann, G., Saalbach, A., Ritter, H.: Semi-Automatic Acquisition and Labelling of Image Data Using SOMs. In: European Symposium on Artificial Neural Networks, pp. 503-508 (2003).
19. Bekel, H., Heidemann, G., Ritter, H.: Interactive image data labeling using selforganizing maps in an augmented reality scenario. Neural Networks 18(5-6), 566-574 (2005). (Pubitemid 43186576)
20. Moehrmann, J., Bernstein, S., Schlegel, T.,Werner, G., Heidemann, G.: Optimizing the Usability of Interfaces for the Interactive Semi-Automatic Labeling of Large Image Data Sets. In: HCI International. LNCS, Springer, Heidelberg (to appear, 2011).
21. Schreck, T., Bernard, J., von Landesberger, T., Kohlhammer, J.: Visual Cluster Analysis of Trajectory Data with Interactive Kohonen Maps. Information Visualization 8, 14-29 (2009).
22. Torkkola, K., Gardner, R.M., Kaysser-Kranich, T., Ma, C.: Self-Organizing Maps in Mining Gene Expression Data. Information Sciences 139(1-2), 79-96 (2001). (Pubitemid 33053359)
23. Kanaya, S., Kinouchi, M., Abe, T., Kudo, Y., Yamada, Y., Nishi, T., Mori, H., Ikemura, T.: Analysis of Codon Usage Diversity of Bacterial Genes with a Self-Organizing Map (SOM): Characterization of Horizontally Transferred Genes with Emphasis on the E. Coli O157 Genome. Gene 276(1-2), 89-99 (2001). (Pubitemid 32925263)
24. Simple-BL SOM Website, http://kanaya.naist.jp/SOM/.
25. Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Self-Organizing Map in Matlab: the SOM toolbox. In: Matlab DSP Conference, pp. 35-40 (1999).
26. SOMVis, TU Wien, http://www.ifs.tuwien.ac.at/dm/somvis-matlab/index.html.
27. Java SOMToolbox, TU Wien, http://www.ifs.tuwien.ac.at/dm/somtoolbox/.
28. Peltarion Synapse, http://www.peltarion.com/products/synapse/.
29. Viscovery SOMine, http://www.viscovery.net/somine/.
30. VisiSOM, http://www.visipoint.fi/visisom.php