Medoids of clusters

Computes the medoids of a cluster solution.

medoids(D, cl)

Arguments

D

square distance matrix (n rows * n columns, i.e. n individuals) or dist object

cl

vector with the clustering solution (its length should be n)

Details

A medoid is a representative object 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).

Value

Returns a numeric vector with the indexes of medoids.

References

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.

Author

Nicolas Robette

See also

dist, cluster, hclust, cutree , pam

Examples

# hierarchical clustering of the Music example data set, 
# partition into 3 groups
# and then computation of the medoids.
data(Music)
temp <- dichotom(Music[,1:5])
d <- dist(temp)
clus <- cutree(hclust(d), 3)
medoids(d, clus)
#> [1] 18 51 25