Abstract
We propose a novel adaptive Self-Organizing Map (SOM). In the introduced approach, the SOM neurons’ neighborhood widths are computed adaptively using the information about the frequencies of occurrences of input patterns in the input space. The neighborhood widths are determined differently for each neuron in the SOM grid. In this way, the proposed SOM properly visualizes the input data, especially, when there are significant differences in frequencies of occurrences of input patterns. The experimental study on real data, on three different datasets, confirms the effectiveness of the proposed adaptive SOM.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer (2001)
Rauber, A., Merkl, D., Dittenbach, M.: The Growing Hierarchical Self-Organizing Map: Exploratory Analysis of High-Dimensional Data. IEEE Transactions on Neural Networks 13(6), 1331–1341 (2002)
Olszewski, D., Kacprzyk, J., Zadrożny, S.: Time Series Visualization Using Asymmetric Self-Organizing Map. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds.) ICANNGA 2013. LNCS, vol. 7824, pp. 40–49. Springer, Heidelberg (2013)
Olszewski, D.: An Experimental Study on Asymmetric Self-Organizing Map. In: Yin, H., Wang, W., Rayward-Smith, V. (eds.) IDEAL 2011. LNCS, vol. 6936, pp. 42–49. Springer, Heidelberg (2011)
Płoński, P., Zaremba, K.: Self-Organising Maps for Classification with Metropolis-Hastings Algorithm for Supervision. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part III. LNCS, vol. 7665, pp. 149–156. Springer, Heidelberg (2012)
Ippoliti, D., Zhou, X.: A-GHSOM: An Adaptive Growing Hierarchical Self Organizing Map for Network Anomaly Detection. Journal of Parallel and Distributed Computing 72(12), 1576–1590 (2012)
Tucci, M., Raugi, M.: A Filter Based Neuron Model for Adaptive Incremental Learning of Self-Organizing Maps. Neurocomputing 74(11), 1815–1822 (2011)
Shah-Hosseini, H.: Binary Tree Time Adaptive Self-Organizing Map. Neurocomputing 74(11), 1823–1839 (2011)
Shah-Hosseini, H., Safabakhsh, R.: TASOM: A New Time Adaptive Self-Organizing Map. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 33(2), 271–282 (2003)
Ressom, H., Wang, D., Natarajan, P.: Adaptive Double Self-Organizing Maps for Clustering Gene Expression Profiles. Neural Networks 16(5-6), 633–640 (2003)
Mulier, F., Cherkassky, V.: Self-Organization as an Iterative Kernel Smoothing Process. Neural Computation 7(6), 1165–1177 (1995)
Heskes, T.: Self-Organizing Maps, Vector Quantization, and Mixture Modeling. IEEE Transactions on Neural Networks 12(6), 1299–1305 (2001)
Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000), Circulation Electronic Pages, http://circ.ahajournals.org/cgi/content/full/101/23/e215
Olszewski, D.: Asymmetric k-Means Algorithm. In: Dobnikar, A., Lotrič, U., Šter, B. (eds.) ICANNGA 2011, Part II. LNCS, vol. 6594, pp. 1–10. Springer, Heidelberg (2011)
Olszewski, D.: k-Means Clustering of Asymmetric Data. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012, Part I. LNCS, vol. 7208, pp. 243–254. Springer, Heidelberg (2012)
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Olszewski, D. (2014). An Improved Adaptive Self-Organizing Map. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-07173-2_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07172-5
Online ISBN: 978-3-319-07173-2
eBook Packages: Computer ScienceComputer Science (R0)