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Multivariate Data Analysis by Means of Self-Organizing Maps

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Ecological Informatics

Abstract

Ecological data range widely in variability, showing non-linear and complex relationships among variables. Although conventional multivariate analyses are useful tools to explore ecological data, data mining by non-linear methods is preferred because a high degree of complexity resides in ecological phenomena. One of these methods is artificial neural networks in machine learning based on biologically inspired learning algorithms. Self-organizing map (SOM) is one of the most popular unsupervised artificial neural networks and are commonly used to seek patterns and clusters in ecological data. SOMs are versatile in analysing non-linear and complex data, which are observed frequently in ecological systems. In this paper, we explain the theory of SOMs and their application in ecological modelling, with a focus on learning processes, visualization, preprocessing of input data, and interpretation of results. We also discuss the advantages and disadvantages of SOM approaches.

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Author information

Authors and Affiliations

  1. Kyung Hee University, Seoul, Republic of Korea

    Young-Seuk Park & Dong-Hwan Kim

  2. Pusan National University, Busan, Republic of Korea

    Tae-Soo Chon

  3. Nakdonggang National Institute of Biological Resources, Gyeongsangbuk-do, Republic of Korea

    Mi-Jung Bae

  4. Université Paul Sabatier, Toulouse, France

    Sovan Lek

Authors
  1. Young-Seuk Park
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  2. Tae-Soo Chon
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  3. Mi-Jung Bae
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  4. Dong-Hwan Kim
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  5. Sovan Lek
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Correspondence to Young-Seuk Park .

Editor information

Editors and Affiliations

  1. School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia

    Friedrich Recknagel

  2. College of University Libraries, University of New Mexico, Albuquerque, New Mexico, USA

    William K. Michener

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Park, YS., Chon, TS., Bae, MJ., Kim, DH., Lek, S. (2018). Multivariate Data Analysis by Means of Self-Organizing Maps. In: Recknagel, F., Michener, W. (eds) Ecological Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-59928-1_12

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