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Cluster Analysis

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Numerical Ecology with R

Part of the book series: Use R ((USE R))

Abstract

In most cases, data exploration (Chap.  2) and the computation of association matrices (Chap.  3) are preliminary steps towards deeper analyses. In this chapter, you will go further by experimenting one of the large groups of analytical methods used in ecology: clustering. Practically, you will: Learn how to choose among various clustering methods and compute them Apply these techniques to the Doubs river data to identify groups of sites and fish species Explore a method of constrained clustering, a powerful modelling approach, where the clustering process is constrained by an external data set

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Notes

  1. 1.

     Available on http://www.bio.umontreal.ca/legendre/indexEn.html and http://sites.google.com/site/miqueldecaceres/software.

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Authors and Affiliations

  1. Département de sciences biologiques, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, Québec, H3C 3J7, Canada

    Daniel Borcard & Pierre Legendre

  2. UMR 6249 Chrono-environnement UFR Sciences et Techniques, Université de Franche-Comté - CNRS, 16, Route de Gray, F-25030, Besançon cedex, France

    François Gillet

Authors
  1. Daniel Borcard
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  2. François Gillet
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  3. Pierre Legendre
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Correspondence to Daniel Borcard .

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Borcard, D., Gillet, F., Legendre, P. (2011). Cluster Analysis. In: Numerical Ecology with R. Use R. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7976-6_4

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