Clustering algorithm defines a particular distance (correlation or euclidean) and a linkage (which, strangely some books call distance - single, complete, average or centroid). Conceptually, correlation or euclidean distance measure distance between two points (but not clusters, perhaps); linkages measure distance between one cluster and other clusters (or points).
So, when the algorithm is applied, how does it matter what distance (correlation/euclidean) I choose, if the dissimilarity and subsequent grouping done only on the basis of linkages?
I know the distance choice matters because it gave me a different answer and dendograms for both measures.
Please help, thanks!