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  • We propose a method for a posteriori evaluation of classification stability which compares the classification of sites in the original data set (a matrix of species by sites) with classifications of subsets of its sites created by without-replacement bootstrap resampling. Site assignments to clusters of the original classification and to clusters of the classification of each subset are compared using Goodman-Kruskal's lambda index. Many resampled subsets are classified and the mean of lambda values calculated for the classifications of these subsets is used as an estimation of classification stability. Furthermore, the mean of the lambda values based on different resampled subsets, calculated for each site of the data set separately, can be used as a measure of the influence of particular sites on classification stability. This method was tested on several artificial data sets classified by commonly used clustering methods and on a real data set of forest vegetation plots.
  • We propose a method for a posteriori evaluation of classification stability which compares the classification of sites in the original data set (a matrix of species by sites) with classifications of subsets of its sites created by without-replacement bootstrap resampling. Site assignments to clusters of the original classification and to clusters of the classification of each subset are compared using Goodman-Kruskal's lambda index. Many resampled subsets are classified and the mean of lambda values calculated for the classifications of these subsets is used as an estimation of classification stability. Furthermore, the mean of the lambda values based on different resampled subsets, calculated for each site of the data set separately, can be used as a measure of the influence of particular sites on classification stability. This method was tested on several artificial data sets classified by commonly used clustering methods and on a real data set of forest vegetation plots. (en)
Title
  • Evaluating the stability of the classification of community data
  • Evaluating the stability of the classification of community data (en)
skos:prefLabel
  • Evaluating the stability of the classification of community data
  • Evaluating the stability of the classification of community data (en)
skos:notation
  • RIV/00216224:14310/11:00050300!RIV12-GA0-14310___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA206/09/0329), Z(MSM0021622416)
http://linked.open...iv/cisloPeriodika
  • 5
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 198246
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14310/11:00050300
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Clustering methods; Vegetation classification strategies; Validation; Bootstrap; Algorithm; Fidelity (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • US - Spojené státy americké
http://linked.open...ontrolniKodProRIV
  • [289037B29CF0]
http://linked.open...i/riv/nazevZdroje
  • Ecography
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 34
http://linked.open...iv/tvurceVysledku
  • Chytrý, Milan
  • Šmarda, Petr
  • Tichý, Lubomír
http://linked.open...ain/vavai/riv/wos
  • 000296972200011
http://linked.open...n/vavai/riv/zamer
issn
  • 0906-7590
number of pages
http://bibframe.org/vocab/doi
  • 10.1111/j.1600-0587.2010.06599.x
http://localhost/t...ganizacniJednotka
  • 14310
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