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Clustering of the structures by using “snakes-&-dragons” approach, or correlation matrix as a signal

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  • Victor P Andreev
  • Gang Liu
  • Jarcy Zee
  • Lisa Henn
  • Gilberto E Flores
  • Robert M Merion

Abstract

Biological, ecological, social, and technological systems are complex structures with multiple interacting parts, often represented by networks. Correlation matrices describing interdependency of the variables in such structures provide key information for comparison and classification of such systems. Classification based on correlation matrices could supplement or improve classification based on variable values, since the former reveals similarities in system structures, while the latter relies on the similarities in system states. Importantly, this approach of clustering correlation matrices is different from clustering elements of the correlation matrices, because our goal is to compare and cluster multiple networks–not the nodes within the networks. A novel approach for clustering correlation matrices, named “snakes-&-dragons,” is introduced and illustrated by examples from neuroscience, human microbiome, and macroeconomics.

Suggested Citation

  • Victor P Andreev & Gang Liu & Jarcy Zee & Lisa Henn & Gilberto E Flores & Robert M Merion, 2019. "Clustering of the structures by using “snakes-&-dragons” approach, or correlation matrix as a signal," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-27, October.
    • Handle: RePEc:plo:pone00:0223267
    DOI: 10.1371/journal.pone.0223267
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    References listed on IDEAS

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    1. World Bank, 2016. "World Development Indicators 2016," World Bank Publications - Books, The World Bank Group, number 23969.
    2. Schäfer Juliane & Strimmer Korbinian, 2005. "A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-32, November.
    3. Peter J. Turnbaugh & Ruth E. Ley & Micah Hamady & Claire M. Fraser-Liggett & Rob Knight & Jeffrey I. Gordon, 2007. "The Human Microbiome Project," Nature, Nature, vol. 449(7164), pages 804-810, October.
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