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The only danger I see with the non-hierachial clustering analysis is if the centroid is ‘skewed’ by a couple of objects. For instance I’ve noticed that the Monte-carlo permutations were ‘nasty’ with the non-hierachial clustering – which is partly due to the reduction of objects in the ordination space (to be expected) but also (i think) the skewed centroids.

I attempted to overcome this issue (partly successfully too) by using the hierachial analysis to first identify obvious outliers, sequentually remove them, reconfirm the data spread with a subsequent hierachial analysis then perform the non-hierachial clustering analysis. My guess is that this increased the reliability of the centroid being in the right spot. Does this make sense?