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Extending the SOM

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Book cover Mapping Financial Stability

Part of the book series: Computational Risk Management ((Comp. Risk Mgmt))

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Abstract

The standard Self-Organizing Map (SOM), while having merit for the task at hand, may be extended in multiple directions, not the least to better meet the demands set by macroprudential oversight and data. Along these lines, with a key focus on temporality, this chapter first discusses the literature on time in SOMs. This is followed by extensions to the standard SOM paradigm. In general, the chapter presents extensions to the SOM paradigm for processing data from the cube representation, i.e., along multivariate, temporal and cross-sectional dimensions, where a focus of emphasis is on a better processing and visualization of time. The motivation and functioning of the extensions is demonstrated with a number of illustrative examples.

As the present now

Will later be past [...]

And the first one now

Will later be last

For the times they are a-changin’.

–Bob Dylan

This chapter is partly based upon previous research. Please see the following works for further information: Sarlin et al. (2012), Sarlin and Eklund (2011), Sarlin (2013b, d, e),  Sarlin and Yao (2013)

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Notes

  1. 1.

    I suggest to illustrate with idle units through some color coding. While idle units have implemented to be colored in gray, these specific cases are not encountered in the experiments performed here.

References

  • Alhoniemi, E., Hollmén, J., Simula, O., & Vesanto, J. (1999). Process monitoring and modeling using the self-organizing map. Integrated Computer Aided Engineering, 6(1), 3–14.

    Google Scholar 

  • Anderson, T., & Goodman, L. (1957). Statistical inference about markov chains. Annals of Mathematical Statistics, 28(1), 89–110.

    Article  Google Scholar 

  • Andrienko, N., Andrienko, V., Bremm, S., Schreck, T., von Landesberger, P., & Keim, D. (2010). Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns. Computer Graphics Forum, 29(3), 913–922.

    Article  Google Scholar 

  • Back, B., Sere, K., & Vanharanta, H. (1998). Managing complexity in large data bases using self-organizing maps. Accounting, Management and Information Technologies, 8(4), 191–210.

    Article  Google Scholar 

  • Back, B., Toivonen, J., Vanharanta, H., & Visa, A. (2001). Comparing numerical data and text information from annual reports using self-organizing maps. International Journal of Accounting Information Systems, 2, 249–259.

    Article  Google Scholar 

  • Barreto, G. (2007). Time series prediction with the self-organizing map: A review. In P. Hitzler & B. Hammer B (Eds.), Perspectives on neural-symbolic integration. Heidelberg: Springer-Verlag.

    Google Scholar 

  • Barreto, G., & Araújo, A. (2001). Time in self-organizing maps: An overview of models. International Journal of Computer Research, 10(2), 139–149.

    Google Scholar 

  • Barreto, G., Araujo, F., & Kremer, S. (2003). A taxonomy for spatiotemporal connectionist networks revisited: The unsupervised case. Neural Computation, 15(6), 1255–1320.

    Article  Google Scholar 

  • Bezdek, J. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press.

    Book  Google Scholar 

  • Chakrabarti, D., Kumar, R., & Tomkins, A. (2006). Evolutionary clustering. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 06) (pp. 554–560). Philadelphia, PA: ACM.

    Google Scholar 

  • Chappell, G., & Taylor, J. (1993). The temporal Kohonen map. Neural Networks, 6, 441–445.

    Article  Google Scholar 

  • Chen, C. (2005). Top 10 unsolved information visualization problems. IEEE Computer Graphics and Applications, 25(4), 12–26.

    Article  Google Scholar 

  • CIELab. (1986). Colorimetry. (CIE Publication No. 15:2).

    Google Scholar 

  • Cottrell, M., Fort, J., & Pagès, G. (1998). Theoretical aspects of the SOM algorithm. Neurocomputing, 21(1–3), 119–138.

    Article  Google Scholar 

  • Cottrell, M., & Letrémy, P. (2005). Missing values: Processing with the Kohonen algorithm. Proceedings of Applied Stochastic Models and Data Analysis (ASMDA 05) (pp. 489–496). France: Brest.

    Google Scholar 

  • Denny, G. W., & Christen, P. (2010). Visualizing temporal cluster changes using relative density self-organizing maps. Knowledge and Information Systems, 25(2), 281–302.

    Google Scholar 

  • Denny., & Squire, D. M. (2005). Visualization of cluster changes by comparing self-organizing maps. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 05). (pp. 410–419). Hanoi, Vietnam.

    Google Scholar 

  • Dunn, J. (1973). A fuzzy relative of the isodata process and its use in detecting compact, well-separated clusters. Cybernetics and Systems, 3, 32–57.

    Google Scholar 

  • Fritzke, B. (1994). A growing neural gas network learns topologies. In G. Tesauro, D. S. Touretzky, & T. K. Leen (Eds.), Advances in Neural Information Processing Systems (Vol. 7, pp. 625–632). MA, Cambridge: MIT Press.

    Google Scholar 

  • Fuertes, J., Domínguez, M., Reguera, P., Prada, M., Díaz, I., & Cuadrado, A. (2010). Visual dynamic model based on self-organizing maps for supervision and fault detection in industrial processes. Engineering Applications of Artificial Intelligence, 23(1), 8–17.

    Article  Google Scholar 

  • Guimarães, G. (2000). Temporal knowledge discovery with self-organizing neural networks. International Journal of Computers, Systems and Signals, 1(1), 5–16.

    Google Scholar 

  • Guimarães, G., Lobo, V., & Moura-Pires, F. (2003). A taxonomy of self-organizing maps for temporal sequence processing. Intelligent Data Analysis, 7(4), 269–290.

    Google Scholar 

  • Guimarães, G., & Ultsch, A. (1999). A method for temporal knowledge conversion. In Proceedings of the International Symposium on Intelligent Data Analysis (IDA 99) (pp. 369–382). Amsterdam, The Netherlands.

    Google Scholar 

  • Guo, D., Chen, J., MacEachren, A., & Liao, K. (2006). A visualization system for space-time and multivariate patterns (VIS-STAMP). IEEE Transactions on Visualization and Computer Graphics, 12(6), 1461–1474.

    Article  Google Scholar 

  • Hagenbuchner, M., Sperduti, A., & Tsoi, A. (2003). Self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks, 14, 191–505.

    Article  Google Scholar 

  • Hammer, B., Micheli, A., Neubauer, N., Sperduti, A., & Strickert, M. (2005). Self organizing maps for time series. Proceedings of the Workshop on Self-Organizing Maps (WSOM 05) (pp. 115–122). Paris: France.

    Google Scholar 

  • Hammer, B., Micheli, A., Sperduti, A., & Strickert, M. (2004). A general framework for unsupervised processing of structured data. Neurocomputing, 57, 3–35.

    Article  Google Scholar 

  • Harrower, M., & Brewer, C. (2003). ColorBrewer.org: An online tool for selecting color schemes for maps. The Cartographic Journal, 40(1), 27–37.

    Article  Google Scholar 

  • Horio, K., & Yamakawa, T. (2001). Feedback self-organizing map and its application to spatio-temporal pattern classification. International Journal of Computational Intelligence and Applications, 1(1), 1–18.

    Article  Google Scholar 

  • Kangas, J. (1990). Time-delayed self-organizing maps. Proceedings of the International Joint Conference on Neural Networks (IJCNN 90) (pp. 331–336). San Diego, USA: IEEE Press.

    Google Scholar 

  • Kangas, J. (1992). Temporal knowledge in locations of activations in a self-organizing map. Proceedings of the International Conference on Artificial Neural Networks (ICANN 92) (pp. 117–120). Brighton, England: IEEE Press.

    Google Scholar 

  • Kaski, S., Venna, J., & Kohonen, T. (2001). Coloring that reveals cluster structures in multivariate data. Australian Journal of Intelligent Information Processing Systems, 60, 2–88.

    Google Scholar 

  • Kohonen, T. (1988). The “neural” phonetic typewriter. Computer, 21(3), 11–22.

    Article  Google Scholar 

  • Kohonen, T. (1991). The hypermap architecture. In T. Kohonen., K. Mäkisara., O. Simula., & J. Kangas (Eds.), Artificial Neural Networks (Vol. II, pp. 1357–1360). Amsterdam, The Netherlands: Elsevier.

    Google Scholar 

  • Kohonen, T. (2001). Self-organizing maps (3rd ed.). Berlin: Springer-Verlag.

    Book  Google Scholar 

  • Koskela, T., Varsta, M., Heikkonen, J., & Kaski, K. (1998). Time series prediction using RSOM with local linear models. International Journal of Knowledge-Based Intelligent Engineering Systems, 2(1), 60–68.

    Google Scholar 

  • Luyssaert, S., Sulkava, M., Raitio, H., & Hollmén, J. (2004). Evaluation of forest nutrition based on large-scale foliar surveys: Are nutrition profiles the way of the future? Journal of Environmental Monitoring, 6(2), 160–167.

    Article  Google Scholar 

  • Martín-del Brío, B., & Serrano-Cinca, C. (1993). Self-organizing neural networks for the analysis and representation of data: Some financial cases. Neural Computing and Applications, 1(2), 193–206.

    Article  Google Scholar 

  • Mayer, R., Abdel Aziz, T., & Rauber, A. (2007). Visualising class distribution on self-organising maps. In Proceedings of the International Conference on Artificial Neural Networks (ICANN 07). Porto, Portugal.

    Google Scholar 

  • Principe, J., & Wang, L. (1995). Non-linear time series modeling with self-organizing feature maps. Proceedings of the IEEE Workshop on Neural Networks for Signal Processing (pp. 11–20). Piscataway, NJ: IEEE Computer Society.

    Google Scholar 

  • Sammon, J. (1969). A non-linear mapping for data structure analysis. IEEE Transactions on Computers, 18(5), 401–409.

    Article  Google Scholar 

  • Sarlin, P. (2013b). Exploiting the self-organizing financial stability map. Engineering Applications of Artificial Intelligence, 26(5–6), 1532–1539.

    Google Scholar 

  • Sarlin, P. (2013d). Self-organizing time map: An abstraction of temporal multivariate patterns. Neurocomputing, 99(1), 496–508.

    Google Scholar 

  • Sarlin, P. (2013e). A self-organizing time map for time-to-event data. In Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM’13) (pp. 230–237). Singapore: IEEE Press.

    Google Scholar 

  • Sarlin, P., & Eklund, T. (2011). Fuzzy clustering of the self-organizing map: Some applications on financial time series. Proceedings of the Workshop on Self-Organizing Maps (WSOM 11) (pp. 40–50). Helsinki, Finland: Springer-Verlag.

    Google Scholar 

  • Sarlin, P., & Eklund, T. (2013). Financial performance analysis of European banks using a fuzzified self-organizing map. International Journal of Knowledge-Based and Intelligent Engineering Systems, 17(3), 223–234.

    Google Scholar 

  • Sarlin, P., & Marghescu, D. (2011). Visual predictions of currency crises using self-organizing maps. Intelligent Systems in Accounting, Finance and Management, 18(1), 15–38.

    Article  Google Scholar 

  • Sarlin, P., & Peltonen, T. (2013). Mapping the state of financial stability. Journal of International Financial Markets, Institutions and Money, 26, 46–56.

    Article  Google Scholar 

  • Sarlin, P., & Yao, Z. (2013). Clustering of the self-organizing time map. Neurocomputing, 121, 317–327.

    Article  Google Scholar 

  • Sarlin, P., Yao, Z., & Eklund, T. (2012). A framework for state transitions on the self-organizing map: Some temporal financial applications. Intelligent Systems in Accounting, Finance and Management, 19(1), 189–203.

    Article  Google Scholar 

  • Strickert, M., & Hammer, B. (2005). Merge SOM for temporal data. Neurocomputing, 64, 39–72.

    Article  Google Scholar 

  • Strickert, M., Hammer, B., & Blohm, S. (2005). Unsupervised recursive sequences processing. Neurocomputing, 63, 69–98.

    Article  Google Scholar 

  • Sulkava, M., & Hollmén, J. (2003). Finding profiles of forest nutrition by clustering of the self-organizing map. Proceedings of the Workshop on Self-Organizing Maps (WSOM 03) (pp. 243–248). Kitakyushu, Japan: Hibikino.

    Google Scholar 

  • Ultsch, A., Guimaraes, G., & Schmidt, W. (1996). Classification and prediction of hail using self-organizing neural networks. In Proceedings of the International Conference on Neural Networks (ICNN 96) (pp. 1622–1627). Washington, USA.

    Google Scholar 

  • Ultsch, A., & Siemon, H. (1990). Kohonen’s self organizing feature maps for exploratory data analysis. In Proceedings of the International Conference on Neural Networks (ICNN 90) (pp. 305–308). Dordrecht, The Netherlands.

    Google Scholar 

  • Varsta, M., Heikkonen, J., & Millán, J. (1997). Context learning with the self organizing map. Proceedings of the Workshop on Self-Organizing Maps (WSOM 97) (pp. 197–202). Finland: Espoo.

    Google Scholar 

  • Vesanto, J., & Alhoniemi, E. (2000). Clustering of the self-organizing map. IEEE Transactions on Neural Networks, 11(3), 586–600.

    Article  Google Scholar 

  • Voegtlin, T. (2002). Recursive self-organizing maps. Neural Networks, 15(8–9), 979–992.

    Article  Google Scholar 

  • Wismüller, A., Verleysen, M., Aupetit, M., & Lee, J. (2010). Recent advances in nonlinear dimensionality reduction, manifold and topological learning. Proceedings of the European Symposium on Artificial Neural Networks (ESANN 10) (pp. 71–80). Belgium: Bruges.

    Google Scholar 

  • Wong, P., Shen, H., Johnson, C., Chen, C., & Ross, R. (2012). The top 10 challenges in extreme-scale visual analytics. IEEE Computer Graphics and Applications, 32(4), 63–77.

    Article  Google Scholar 

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Authors and Affiliations

  1. Centre of Excellence SAFE, Goethe University Frankfurt, Grüneburgplatz 1, 60323, Frankfurt am Main, Germany

    Peter Sarlin

  2. RiskLab Finland, IAMSR Åbo Akademi University, Turku, Finland

    Peter Sarlin

  3. Arcada University of Applied Sciences, Helsinki, Finland

    Peter Sarlin

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  1. Peter Sarlin
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Correspondence to Peter Sarlin .

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Sarlin, P. (2014). Extending the SOM. In: Mapping Financial Stability. Computational Risk Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54956-4_6

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  • DOI: https://doi.org/10.1007/978-3-642-54956-4_6

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