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Auteur Carteron, A.; Jeanmougin, M.; Leprieur, F.; Spatharis, S. url  doi
  Titre Assessing the efficiency of clustering algorithms and goodness-of-fit measures using phytoplankton field data Type Article scientifique
  Année 2012 Publication Revue Abrégée Ecol. Inform.  
  Volume 9 Numéro Pages 64-68  
  Mots-Clés 2-norm; Beta diversity; Cophenetic correlation coefficient; Dendrogram; Upgma; Ward's algorithm; beta-diversity; blooms; classification; communities; cophenetic correlation; environment; forest; functional diversity; som  
  Résumé Investigation of patterns in beta diversity has received increased attention over the last years particularly in light of new ecological theories such as the metapopulation paradigm and metacommunity theory. Traditionally, beta diversity patterns can be described by cluster analysis (i.e. dendrograms) that enables the classification of samples. Clustering algorithms define the structure of dendrograms, consequently assessing their performance is crucial. A common, although not always appropriate approach for assessing algorithm suitability is the cophenetic correlation coefficient c. Alternatively the 2-norm has been recently proposed as an increasingly informative method for evaluating the distortion engendered by clustering algorithms. In the present work, the 2-norm is applied for the first time on field data and is compared with the cophenetic correlation coefficient using a set of 105 pairwise combinations of 7 clustering methods (e.g. UPGMA) and 15 (dis)similarity/distance indices (e.g. Jaccard index). In contrast to the 2-norm, cophenetic correlation coefficient does not provide a clear indication on the efficiency of the clustering algorithms for all combinations. The two approaches were not always in agreement in the choice of the most faithful algorithm. Additionally, the 2-norm revealed that UPGMA is the most efficient clustering algorithm and Ward's the least. The present results suggest that goodness-of-fit measures such as the 2-norm should be applied prior to clustering analyses for reliable beta diversity measures. (C) 2012 Elsevier B.V. All rights reserved.  
  Auteur institutionnel Thèse  
  Editeur Lieu de Publication Éditeur  
  Langue English Langue du Résumé Titre Original  
  Éditeur de collection Titre de collection Titre de collection Abrégé  
  Volume de collection Numéro de collection Edition  
  ISSN 1574-9541 ISBN Médium  
  Région Expédition Conférence  
  Notes Approuvé pas de  
  Numéro d'Appel MARBEC @ isabelle.vidal-ayouba @ collection 457  
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