Abstract
Self-Organizing Maps have been shown to be a powerful unsupervised learning a tool in the analysis of complex high dimensional data. SOMs are capable of performing topological mapping, clustering and dimensionality reduction in order to effectively visualize and understand data and it is desirable to apply these techniques to time–series data. In this project a novel approach to time-series learning using Concentric Multi-Sphere SOMs has been expanded and generalized into a unified framework in order to thoroughly test the learning capabilities. It is found that Quantization and Topological Error are not suitable to test the learning performance of the algorithms and it is suggested that future work focus on developing new error measures and learning algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bação, F., Lobo, V.: Introduction to Kohonen’s Self-Organizing Maps, Universidade Nova De Lisboa, Portugal (2010), http://edugi.uji.es/Bacao/SOM%20Tutorial.pdf
Kohonen, T.: Self-Organizing Maps. Springer (1995)
Nishio, H., Altaf-Ui-Amin, M.D., Kurokawa, K., Kanaya, S.: Spherical SOM and Arrangement of Neurons Using Helix on Sphere. IPSJ Digital Courier 2, 133–137 (2006)
Leontitsis, A., Sangole, A.P.: Estimating an optimal neighborhood size in the spherical self-organizing feature map. Int. Journal of Computational Intelligence 2, 94–98 (2005)
Sarle, W.S.: Neural Network FAQ Part 1 (2002), ftp://ftp.sas.com/pub/neural/FAQ.html
Wu, Y., Takatsuka, M.: Spherical self-organizing map using efficient indexed geodesic data structure. Neural Networks 19, 900–910 (2006)
Ritter, H.: Self-organizing maps on non-euclidean spaces. Kohonen Maps, 97–108 (1999)
Sangole, A., Knopf, G.K.: Visualization of randomly ordered numeric data sets using spherical self-organizing feature maps. Computers & Graphics 27, 963–976 (2003)
Sugii, Y., Satoh, H., Yu, D., Matsuura, Y., Tokutaka, H., Seno, M.: Spherical self-organizing map as a helpful tool to identify category-specific cell surface markers. Biochemical and Biophysical Research Communications 376 (2008)
Blazejewski, A., Coggins, R.: Application of self-organizing maps to clustering of high-frequency financial data. In: 2nd Workshop on Australasian Information Security, Data Mining and Web Intelligence, and Software Internationalisation, vol. 32, pp. 85–90 (2004)
Sharma, N., Gedeon, T.D.: Objective measures, sensors and computational techniques for stress recognition and classification: A survey. Computer Methods and Programs in Biomedicine 108(3), 1287–1301 (2012), doi:10.1016/j.cmpb.2012.07.003
Sharma, N., Gedeon, T.: Computational Models of Stress in Reading Using Physiological and Physical Sensor Data. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part I. LNCS (LNAI), vol. 7818, pp. 111–122. Springer, Heidelberg (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gedeon, T., Paget, L., Zhu, D. (2013). Distance Metrics for Time-Series Data with Concentric Multi-Sphere Self Organizing Maps. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_94
Download citation
DOI: https://doi.org/10.1007/978-3-642-42042-9_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42041-2
Online ISBN: 978-3-642-42042-9
eBook Packages: Computer ScienceComputer Science (R0)