ReDSOM: Relative density visualization of temporal changes in cluster structures using self-organizing maps

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2008, Pages 173-182

  Denny, ^a,b   Williams, Graham J ^c   Christen, Peter ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b UNIVERSITY OF INDONESIA   (Indonesia)

^c Australian Taxation Office Canberra   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER CENTROIDS; CLUSTER DENSITIES; CLUSTER STRUCTURE; DATA SETS; DISTANCE MATRICES; REAL LIFE DATASETS; RELATIVE DENSITY; SYNTHETIC DATASETS; TEMPORAL CHANGE; VISUALIZATION METHOD; WORLD BANK;

CONFORMAL MAPPING; INFORMATION MANAGEMENT; MINING; STRENGTH OF MATERIALS; TAXATION; VISUALIZATION;

SELF ORGANIZING MAPS;

EID: 67049100143     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2008.34     Document Type: Conference Paper

References (26)

1. G. Adomavicius and J. Bockstedt. C-trend: Temporal cluster graphs for identifying and visualizing trends in multi-attribute transactional data. IEEE TKDE, 20(6):721-735, 2008. (Pubitemid 351606068)
2. C. Aggarwal, J. Han, J. Wang, and P. Yu. A framework for clustering evolving data streams. VLDB, 29:81-92, 2003.
3. D. Chakrabarti, R. Kumar, and A. Tomkins. Evolutionary clustering. In ACM SIGKDD 2006, pages 554-560, New York, NY, USA, 2006. (Pubitemid 44535554)
4. G. Deboeck and T. Kohonen. Visual Explorations in Financewith Self-Organizing Maps. Springer-Verlag, London, 1998.
5. Denny and D. M. Squire. Visualization of cluster changes by comparing Self-Organizing Maps. In PAKDD 2005, volume 3518 of LNCS, pages 410-419. Springer, 2005.
6. Denny, G. J. Williams, and P. Christen. Exploratory hot spot profile analysis using interactive visual drill-down selforganizing maps. In PAKDD 2008, volume 5012 of LNCS, pages 536-543. Springer, 2008. (Pubitemid 351776345)
7. V. Ganti, J. Gehrke, R. Ramakrishnan, and W.-Y. Loh. A framework for measuring differences in data characteristics. Journal of Computer and System Sciences, 64:542-578, May 2002.
8. J. Han and M. Kamber. Data Mining: Concepts and Techniques (second edition). Morgan Kaufmann, San Francisco, CA, 2006.
9. S. Hido, T. Idé, H. Kashima, H. Kubo, and H. Matsuzawa. Unsupervised change analysis using supervised learning. In PAKDD 2008, volume 5012 of LNCS, pages 148-159. Springer, 2008. (Pubitemid 351776314)
10. J. Iivarinen, T. Kohonen, J. Kangas, and S. Kaski. Visualizing the clusters on the Self-Organizing Map. In Conference on AI Research in Finland, volume 12, pages 122-126. Finnish AI Society, 1994.
11. S. Kaski and K. Lagus. Comparing Self-Organizing Maps. In ICANN'96, Bochum, Germany, volume 1112 of LNCS, pages 809-814. Springer, Berlin, 1996.
12. D. A. Keim. Information visualization and visual data mining. IEEE Trans. Vis. Comput. Graph., 8(1):1-8, 2002. (Pubitemid 34573478)
13. E. Keogh, J. Lin, and W. Truppel. Clustering of time series subsequences is meaningless: Implications for previous and future research. In IEEE ICDM 2003, page 115, Washington, DC, USA, 2003.
14. T. Kohonen. Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43:59-69, 1982.
15. T. Kohonen. Self-Organizing Maps (Third Edition), volume 30 of Springer Series in Information Sciences. Springer, Berlin, Heidelberg, 2001.
16. P. Lingras, M. Hogo, M. Snorek, and C. West. Temporal analysis of clusters of supermarket customers: conventional vs. interval set approach. Inf. Sci., 172(1-2):215-240, 2005.
17. H. Ritter, T. Martinetz, and K. Schulten. Neural Computation and Self-Organizing Maps; An Introduction. Addison- Wesley Longman Publishing, Boston, USA, 1992.
18. J. F. Roddick and M. Spiliopoulou. A survey of temporal knowledge discovery paradigms and methods. IEEE TKDE, 14(4):750-767, 2002. (Pubitemid 34935987)
19. J. C. Schlimmer and R. H. Granger. Incremental learning from noisy data. Machine Learning, 1(3):317-354,1986.
20. The Treasury - Australian Government. Press release no. 066, July 2006.http://www.treasurer.gov.au/.
21. V. Tryba, S. Metzen, and K. Goser. Designing basic integrated circuits by Self-Organizing Feature Maps. In Neuro- N̂imes'89. Intl. Workshop on Neural Networks and their Applications, pages 225-235, Nanterre, France, November 1989. ARC; SEE, EC2.
22. J. Vesanto and E. Alhoniemi. Clustering of the Self- Organizing Map. IEEE TNN, 11(3):586-600, May 2000.
23. G. Widmer and M. Kubat. Learning in the presence of concept drift and hidden contexts. Machine Learning, 23(1):69-101, 1996. (Pubitemid 126737384)
24. World Bank. World Development Indicators 2003. The World Bank, Washington DC, 2003.
25. K. Yamanishi, J.-I. Takeuchi, G.Williams, and P. Milne. Online unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Mining and Knowledge Discovery, 8(3):275-300, 2004. (Pubitemid 39019964)
26. R. Zagha and G. T. Nankani, editors. Economic Growth in the 1990s: Learning from a Decade of Reform. World Bank Publications, Washington, DC, 2005.