Publication details

Evaluating the stability of the classification of community data



Year of publication 2011
Type Article in Periodical
Magazine / Source Ecography
MU Faculty or unit

Faculty of Science

Web Fulltext on Wiley Online Library
Field Botany
Keywords Clustering methods; Vegetation classification strategies; Validation; Bootstrap; Algorithm; Fidelity
Description 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.
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