cl_pam  R Documentation 
Compute kmedoids partitions of clusterings.
cl_pam(x, k, method = "euclidean", solver = c("pam", "kmedoids"))
x 
an ensemble of partitions or hierarchies, or something
coercible to that (see 
k 
an integer giving the number of classes to be used in the partition. 
method 
a character string or a function, as for argument

solver 
a character string indicating the kmedoids solver
to be employed. May be abbreviated. If 
An optimal kmedoids partition of the given cluster ensemble is defined as a partition of the objects x_i (the elements of the ensemble) into k classes C_1, …, C_k such that the criterion function L = ∑_{l=1}^k \min_{j \in C_l} ∑_{i \in C_l} d(x_i, x_j) is minimized.
Such secondary partitions (e.g., Gordon & Vichi, 1998) are obtained by computing the dissimilarities d of the objects in the ensemble for the given dissimilarity method, and applying a dissimilaritybased kmedoids solver to d.
An object of class "cl_pam"
representing the obtained
“secondary” partition, which is a list with the following
components.
cluster 
the class ids of the partition. 
medoid_ids 
the indices of the medoids. 
prototypes 
a cluster ensemble with the k prototypes (medoids). 
criterion 
the value of the criterion function of the partition. 
description 
a character string indicating the dissimilarity method employed. 
L. Kaufman and P. J. Rousseeuw (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.
A. D. Gordon and M. Vichi (1998). Partitions of partitions. Journal of Classification, 15, 265–285. doi: 10.1007/s003579900034.
cl_pclust
for more general prototypebased partitions of
clusterings.
data("Kinship82") party < cl_pam(Kinship82, 3, "symdiff") ## Compare results with tables 5 and 6 in Gordon & Vichi (1998). party lapply(cl_prototypes(party), cl_classes) table(cl_class_ids(party))
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