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Example sentences for: medoids

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As implemented in the cluster package in R and S-Plus, the PAM function takes as its arguments a dissimilarity matrix (for example the Euclidean distance matrix as used here) and a prespecified number of clusters K. The PAM procedure is based on the search for K representative objects, or medoids, among the observations to be clustered.
The algorithm first looks for a good initial set of medoids, then finds a local minimum for the objective function, that is, a solution such that there is no single switch of an observation with a medoid that will decrease the objective.
After finding a set of K medoids, K clusters are constructed by assigning each observation to the nearest medoid.
Partitioning around medoids
The goal is to find K medoids that minimize the sum of the dissimilarities of the observations to their closest medoid.