Abstract
We are interested in practical tools for the quantitative evaluation of self-organizing maps (SOMs). Recently it has been argued that any quality measure for SOMs needs to evaluate the embedding or coverage of a map as well as its topological quality. Over the years many different quality measures for self-organizing maps have been proposed. However, many of these only measure one aspect of a SOM or are computationally very expensive or both. Here we present a novel, computationally efficient statistical approach to the evaluation of SOMs. Our approach measures both the embedding and the topological quality of a SOM.
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Notes
- 1.
The 3.0 version should be available on CRAN by August 2015.
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Acknowledgments
The author would like to thank Gavino Puggioni for suggesting the non-parametric goodness of fit tests.
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Hamel, L. (2016). SOM Quality Measures: An Efficient Statistical Approach. In: Merényi, E., Mendenhall, M., O'Driscoll, P. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-319-28518-4_4
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DOI: https://doi.org/10.1007/978-3-319-28518-4_4
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