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Comparing three classification strategies for use in ecology
Author(s) -
Belbin Lee,
McDonald Cam
Publication year - 1993
Publication title -
journal of vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 115
eISSN - 1654-1103
pISSN - 1100-9233
DOI - 10.2307/3235592
Subject(s) - upgma , hierarchical clustering , ecology , cluster analysis , geography , data mining , computer science , artificial intelligence , biology , biochemistry , gene , genotype
. We compare three common types of clustering algorithms for use with community data. TWINSPAN is divisive hierarchical, flexible‐UPGMA is agglomerative and hierarchical, and ALOC is non‐hierarchical. A balanced design six‐factor model was used to generate 480 data sets of known characteristics. Recovery of the embedded clusters suggests that both flexible UPGMA and ALOC are significantly better than TWINSPAN. No significant difference existed between flexible UPGMA and ALOC.