Hierarchical clusters of vegetation types
Author(s) -
Christopher S. Wallace,
M. B. Dale
Publication year - 2005
Publication title -
community ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.388
H-Index - 31
eISSN - 1588-2756
pISSN - 1585-8553
DOI - 10.1556/comec.6.2005.1.7
Subject(s) - hierarchical clustering , hierarchical database model , hierarchical organization , homogeneous , hierarchical control system , computer science , animal ecology , multilevel model , cluster analysis , vegetation (pathology) , data mining , ecology , artificial intelligence , mathematics , biology , machine learning , medicine , pathology , management , control (management) , combinatorics , economics
In this paper, we examine possible sources of hierarchical (nested) structure in vegetation data. We then use the Minimum Message length principle to provide a rational means of comparing hierarchical and non-hierarchical clustering. The results indicate that, with the data used, a hierarchical solution was not as efficient as a nonhierarchical one. However, the hierarchical solution seems to provide a more comprehensible solution, separating first isolated types, probably caused from unusual contingent events, then subdividing the more diverse areas before finally subdividing the less diverse. By presenting this in 3 stages, the complexity of the non-hierarchical result is avoided. The result also suggests that a hierarchical analysis may be useful in determining homogeneous areas
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