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Ecological Informatics: An Introduction Read chapter Read first chapter

2018 | OriginalPaper | Chapter

12. Multivariate Data Analysis by Means of Self-Organizing Maps

Authors : Young-Seuk Park, Tae-Soo Chon, Mi-Jung Bae, Dong-Hwan Kim, Sovan Lek

Published in: Ecological Informatics

Publisher: Springer International Publishing

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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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previous chapter Uncertainty Analysis by Bayesian Inference
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Metadata
Title
Multivariate Data Analysis by Means of Self-Organizing Maps
Authors
Young-Seuk Park
Tae-Soo Chon
Mi-Jung Bae
Dong-Hwan Kim
Sovan Lek
Copyright Year
2018
Book
Ecological Informatics

Print ISBN: 978-3-319-59926-7

Electronic ISBN: 978-3-319-59928-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-59928-1_12