Computes the medoids of a cluster solution.
square distance matrix (n rows * n columns, i.e. n individuals) or
vector with the clustering solution (its length should be n)
Medoids are representative objects of a cluster whose average dissimilarity to all the objects in the cluster is minimal. Medoids are always members of the data set (contrary to means or centroids).
Returns a numeric vector with the indexes of medoids.
Kaufman, L. and Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.
Anja Struyf, Mia Hubert & Peter J. Rousseeuw (1996). "Clustering in an Object-Oriented Environment". Journal of Statistical Software.
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