K-Medoids Partitions of Clusterings
Compute k-medoids 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 k-medoids solver
to be employed. May be abbreviated. If |
An optimal k-medoids 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 dissimilarity-based k-medoids 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 prototype-based 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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.