Abstract
In this chapter, we introduce the Self-Organizing Map (SOM) from the viewpoint of Chance Discovery. The SOM paradigm supports several principal parts of Chance Discovery: visualization of temporal multivariate data, discovering rare clusters bridging frequent ones, detecting the degree of event rarity or outliers, and dealing with continuously evolving structures of real world data. Here, we further enhance the standard SOM paradigm by combining it with network analysis. Thus, we enable a simultaneous view of the data topology of the SOM and a network topology of relationships between objects on the SOM. The usefulness of the Self-Organizing Network Map (SONM) for Chance Discovery is demonstrated on a dataset of macro-financial measures. While the standard SOM visualizes country-specific vulnerabilities by positions on the map, the SONM also includes bilateral financial exposures to show the size of linkages between economies and chances of shock propagation from one country to the rest of the world.
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References
Ohsawa, Y.: Chance Discovery for Making Decisions in Complex Real World. New Generation Computing 20(2), 143–163 (2002)
Tsang, E.P.K., Markose, S., Er, H.: Chance discovery in stock index option and future arbitrage. New Mathematics and Natural Computation 1(3), 435–447 (2005)
Ohsawa, Y.: Modelling the process of chance discovery. In: Ohsawa, Y., McBurney, P. (eds.) Chance Discovery, pp. 2–15. Springer, Heidelberg (2003)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, California (1993)
Greenacre, M.J.: Correspondence Analysis in Practice. Chapman & Hall, London (2007)
Ohsawa, Y., Benson, N.E., Yachida, M.: KeyGraph: Automatic Indexing by Cooccurrence Graph based on Building Construction Metaphor. In: Proc. Advanced Digital Library Conference, pp. 12–18. IEEE Press, Los Alamitos (1998)
Abe, A., Hagita, N., Furutani, M., Furutani, Y., Matsuoka, R.: An interface for medical diagnosis support. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part II. LNCS (LNAI), vol. 4693, pp. 909–916. Springer, Heidelberg (2007)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 66, 59–69 (1982)
Kohonen, T.: Self-Organizing Maps. Springer, Berlin (2001)
Ultsch, A., Siemon, H.P.: Kohonen’s self organizing feature maps for exploratory data analysis. In: Proceedings of the International Conference on Neural Networks, pp. 305–308. Kluwer, Dordrecht (1990)
Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)
Ward, J.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58, 236–244 (1963)
Matsuo, Y.: Prediction, Forecasting and Chance Discovery. In: Ohsawa, Y., McBurney, P. (eds.) Chance Discovery, pp. 30–43. Springer, Heidelberg (2003)
Boulet, R., Jouve, B., Rossi, F., Villa, N.: Batch kernel SOM and related Laplacian methods for social network analysis. Neurocomputing 71(7-9), 1257–1273 (2008)
Goda, S., Ohsawa, Y.: Chance Discovery in Credit Risk Management - Time Order Method and Directed KeyGraph for Estimation of Chain Reaction Bankruptcy Structure. In: Satoh, K., Inokuchi, A., Nagao, K., Kawamura, T. (eds.) JSAI 2007. LNCS (LNAI), vol. 4914, pp. 247–254. Springer, Heidelberg (2008)
Sarlin, P., Peltonen, T.A.: Mapping the State of Financial Stability. ECB Working Paper, No. 1382 (September 2011)
Lo Duca, M., Peltonen, T.A.: Macro-Financial Vulnerabilities and Future Financial Stress — Assessing Systemic Risks and Predicting Systemic Events. ECB Working Paper, No. 1311 (2011)
Borio, C., Lowe, P.: Asset Prices, Financial and Monetary Stability: Exploring the Nexusd. BIS Working Papers, No. 114 (2002)
Borio, C., Lowe, P.: Securing Sustainable Price Stability: Should Credit Come Back from the Wilderness? BIS Working Papers, No. 157 (2004)
Sammon Jr., J.W.: A Non-Linear Mapping for Data Structure Analysis. IEEE Transactions on Computers 18(5), 401–409 (1969)
Sarlin, P., Eklund, T.: Fuzzy Clustering of the Self-Organizing Map: Some Applications on Financial Time Series. In: Laaksonen, J., Honkela, T. (eds.) WSOM 2011. LNCS, vol. 6731, pp. 40–50. Springer, Heidelberg (2011)
Sarlin, P., Eklund, T.: Financial Performance Analysis of European Banks using a Fuzzified Self-Organizing Map. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part II. LNCS, vol. 6882, pp. 186–195. Springer, Heidelberg (2011)
Sarlin, P., Yao, Z., Eklund, T.: Probabilistic Modeling of State Transitions on the Self-Organizing Map: Some Temporal Financial Applications. In: Proc. of the 45th Hawaii International Conference on System Sciences, HICSS 2012 (forthcoming, 2012)
Chappell, G., Taylor, J.: The temporal Kohonen map. Neural Networks 6, 441–445 (1993)
Strickert, M., Hammer, B.: Merge SOM for temporal data. Neurocomputing 64, 39–72 (2005)
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Sarlin, P. (2013). Chance Discovery with Self-Organizing Maps: Discovering Imbalances in Financial Networks. In: Ohsawa, Y., Abe, A. (eds) Advances in Chance Discovery. Studies in Computational Intelligence, vol 423. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30114-8_4
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DOI: https://doi.org/10.1007/978-3-642-30114-8_4
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