Abstract
Self-Organizing Map (SOM) is a widely used algorithm in artificial neural network for classification. Despite the general success of this algorithm, there are several limitations which some of them are poor classification accuracy and slow rates of convergence when the standard lattice topology and distance measurement are implemented. This paper investigates the performance of SOM using different topologies and different distance measurements. The results obtained showed that SOM with hexagonal topology and Euclidean distance measurement outperforms other topologies and distance measurement using at any scale datasets.
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
References
Kohonen, T. (1997). Self-organizing maps. In Springer series in information sciences. Berlin, Heidelberg, New York: Springer.
Pastukhov, A., & Prokofiev, A. (2016). Kohonen self-organizing map application to representative sample formation in the training of the multilayer perceptron. St. Petersburg Polytechnical University Journal: Physics and Mathematics 2, 134–143.
ChandraShekar, B. H., & Shoba, G. (2009). Classification of documents using Kohonen’s self-organizing map. International Journal of Computer Theory and Engineering, 1(5), 610–613.
Dua, D., & Karra Taniskidou, E. (2017). UCI machine learning repository http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science.
Jaramillo-Vacio, R., Ochoa-Zezzatti, A., & Figueroa-Godoy, F. (2015). Self-organizing map improved for classification of partial discharge using desirability function. International Journal of Combinatorial Optimization Problems and Informatics, 6(3), 49–65.
Chaudhary, V., Ahlawat, A. K., & Bhatia, R. S. (2013). An efficient self-organizing map learning algorithm with winning frequency of neurons for clustering application. In Proceedings of International Advanced Computing Conference (pp. 664–668). IEEE.
Singh, A., Yadav, A., Rana, A. (2013). K-means with three different distance metrics. International Journal of Computer Applications, 67(10).
Lohninger, H. (2018). Kohonen Network—Background Information, http://www.lohninger.com/helpcsuite/kohonen_network_-_background_information.htm.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ramiah, S. (2019). Effect of Lattice Topologies and Distance Measurements in Self-Organizing Map for Better Classification. In: Shetty, N., Patnaik, L., Nagaraj, H., Hamsavath, P., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing, vol 882. Springer, Singapore. https://doi.org/10.1007/978-981-13-5953-8_16
Download citation
DOI: https://doi.org/10.1007/978-981-13-5953-8_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5952-1
Online ISBN: 978-981-13-5953-8
eBook Packages: EngineeringEngineering (R0)