Abstract
Results of cluster analysis usually depend to a large extent on the choice of a clustering method. Clustering with constraint (time or space) is a way of restricting the set of possible solutions to those that make sense in terms of these constraints. Time and space contiguity are so important in ecological theory that their imposition as an a priori model during clustering is reasonable. This paper reviews various methods that have been proposed for clustering with constraint, first in one dimension (space or time), then in two or more dimensions (space). It is shown, using autocorrelated simulated data series, that if patches do exist, constrained clustering always recovers a larger fraction of the information than the unconstrained equivalent. The comparison of autocorrelated to uncorrected data series also shows that one can tell, from the results of agglomerative constrained clustering, whether the patches delineated by constrained clustering are real. Finally, it is shown how constrained clustering can be extended to domains other than space or time.
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Legendre, P. (1987). Constrained Clustering. In: Legendre, P., Legendre, L. (eds) Develoments in Numerical Ecology. NATO ASI Series, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-70880-0_7
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DOI: https://doi.org/10.1007/978-3-642-70880-0_7
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