multiview_pam_gen: Multiview PAM (K-medoids) generation

In ConsensusClustering: Consensus Clustering

Multiview PAM (K-medoids) generation

Description

Multiview PAM (K-medoids) generation

Usage

multiview_pam_gen(
  X,
  rep = 10,
  range.k = c(2, 5),
  is.distance = FALSE,
  method = "random",
  sample.set = NA
)

Arguments

X	List of input data matrices of Sample x feature or distance matrices. The length of X is equal to Nviews
rep	number of repeats
range.k	vector of minimum and maximum values for k c(min, max)
is.distance	binary balue indicating if the input X is distance
method	method for the choice of k at each repeat c("random", "silhouette")
sample.set	vector of samples the clustering is being applied on. can be names or indices. if sample.set is NA, it considers all the datasets have the same samples with the same order

Details

At each repeat, k is selected randomly or based on the best silhouette width from a discrete uniform distribution between range.k[1] and range.k[2]. Then PAM clustering is applied and result is returned.

Value

matrix of clusterings Nsample x (Nrepeat x Nviews)

Examples

data = multiview_clusters (n = c(40,40,40), hidden.dim = 2, observed.dim = c(2,2,2),
sd.max = .1, sd.noise = 0, hidden.r.range = c(.5,1))
X_observation = data[["observation"]]
Clusters = multiview_pam_gen(X_observation)

ConsensusClustering documentation built on Sept. 11, 2024, 6:38 p.m.