multiview_cluster_gen {ConsensusClustering}

Multiview cluster generation

Description

Multiview cluster generation

Usage

multiview_cluster_gen(
  X,
  func,
  rep = 10,
  param,
  is.distance = FALSE,
  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

func

custom function that accepts X and a parameter that return a vector of clusterings. cluster_func <- function(X, param)

rep

number of repeats

param

vector of parameters

is.distance

binary balue indicating if the input X[i] is distance

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 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"]]
cluster_func = function(X,rep,param){return(multi_kmeans_gen(X,rep=rep,range.k=param))}
Clusters = multiview_cluster_gen(X_observation, func = cluster_func, rep = 10, param = c(2,4))