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Advances in Self-Organizing Maps and Learning Vector Quantization pp 49–59Cite as

SOM Quality Measures: An Efficient Statistical Approach

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 428))

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. 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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Authors and Affiliations

  1. Department of Computer Science and Statistics, University of Rhode Island, RI, 02881, Kingston, USA

    Lutz Hamel

Authors
  1. Lutz Hamel
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Correspondence to Lutz Hamel .

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Editors and Affiliations

  1. Department of Statistics, Rice University, Houston, Texas, USA

    Erzsébet Merényi

  2. Department of Electrical and Computer En, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USA

    Michael J. Mendenhall

  3. Applied Physics, Rice University, Houston, Texas, USA

    Patrick O'Driscoll

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28517-7

  • Online ISBN: 978-3-319-28518-4

  • eBook Packages: EngineeringEngineering (R0)

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