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Self-Organizing Map Convergence

Self-Organizing Map Convergence

Robert Tatoian, Lutz Hamel
Copyright: © 2018 |Volume: 9 |Issue: 2 |Pages: 24
ISSN: 1947-959X|EISSN: 1947-9603|EISBN13: 9781522544296|DOI: 10.4018/IJSSMET.2018040103
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MLA

Tatoian, Robert, and Lutz Hamel. "Self-Organizing Map Convergence." IJSSMET vol.9, no.2 2018: pp.61-84. http://doi.org/10.4018/IJSSMET.2018040103

APA

Tatoian, R. & Hamel, L. (2018). Self-Organizing Map Convergence. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 9(2), 61-84. http://doi.org/10.4018/IJSSMET.2018040103

Chicago

Tatoian, Robert, and Lutz Hamel. "Self-Organizing Map Convergence," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) 9, no.2: 61-84. http://doi.org/10.4018/IJSSMET.2018040103

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Abstract

Self-organizing maps are artificial neural networks designed for unsupervised machine learning. Here in this article, the authors introduce a new quality measure called the convergence index. The convergence index is a linear combination of map embedding accuracy and estimated topographic accuracy and since it reports a single statistically meaningful number it is perhaps more intuitive to use than other quality measures. The convergence index in the context of clustering problems was proposed by Ultsch as part of his fundamental clustering problem suite as well as real world datasets. First demonstrated is that the convergence index captures the notion that a SOM has learned the multivariate distribution of a training data set by looking at the convergence of the marginals. The convergence index is then used to study the convergence of SOMs with respect to the different parameters that govern self-organizing map learning. One result is that the constant neighborhood function produces better self-organizing map models than the popular Gaussian neighborhood function.

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