A dissimilarity matrix records pairwise separation of observations or profiles. Usually it is symmetric, nonnegative and zero on the diagonal. A metric space additionally requires the triangle inequality and separation of distinct profiles. Agglomerative hierarchical clustering can be applied to many dissimilarity matrices that are not Euclidean distance matrices; the claimed geometry must be checked separately.
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