multiview_kmeans_gen {ConsensusClustering}

Multiview K-means generation

Description

Multiview K-means generation

Usage

multiview_kmeans_gen(X, rep = 10, range.k = c(2, 5), method = "random")

Arguments

X

List of input data matrices of Sample x feature. 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)

method

method for the choice of k at each repeat c("random", "silhouette")

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 k-means 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_kmeans_gen(X_observation)