consensusCluster {coca}

Consensus clustering

Description

This function allows to perform consensus clustering using the k-means clustering algorithm, for a fixed number of clusters. We consider the number of clusters K to be fixed.

Usage

consensusCluster(
  data = NULL,
  K = 2,
  B = 100,
  pItem = 0.8,
  clMethod = "hclust",
  dist = "euclidean",
  hclustMethod = "average",
  sparseKmeansPenalty = NULL,
  maxIterKM = 1000
)

Arguments

data

N X P data matrix

K

Number of clusters.

B

Number of iterations.

pItem

Proportion of items sampled at each iteration.

clMethod

Clustering algorithm. Can be "hclust" for hierarchical clustering, "kmeans" for k-means clustering, "pam" for partitioning around medoids, "sparse-kmeans" for sparse k-means clustering or "sparse-hclust" for sparse hierarchical clustering. Default is "hclust". However, if the data contain at least one covariate that is a factor, the default clustering algorithm is "pam".

dist

Distance used for hierarchical clustering. Can be "pearson" (for 1 - Pearson correlation), "spearman" (for 1- Spearman correlation), any of the distances provided in stats::dist() (i.e. "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski"), or a matrix containing the distances between the observations.

hclustMethod

Hierarchical clustering method. Default is "average". For more details see ?hclust.

sparseKmeansPenalty

If the selected clustering method is "sparse-kmeans", this is the value of the parameter "wbounds" of the "KMeansSparseCluster" function. The default value is the square root of the number of variables.

maxIterKM

Number of iterations for the k-means clustering algorithm.

Value

The output is a consensus matrix, that is a symmetric matrix where the element in position (i,j) corresponds to the proportion of times that items i and j have been clustered together.

Author(s)

Alessandra Cabassi [e-mail]

References

Monti, S., Tamayo, P., Mesirov, J. and Golub, T., 2003. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine learning, 52(1-2), pp.91-118.

Witten, D.M. and Tibshirani, R., 2010. A framework for feature selection in clustering. Journal of the American Statistical Association, 105(490), pp.713-726.

Examples

# Load one dataset with 300 observations, 2 variables, 6 clusters
data <- as.matrix(read.csv(system.file("extdata", "dataset1.csv",
package = "coca"), row.names = 1))

# Compute consensus clustering with K=5 clusters
cm <- consensusCluster(data, K = 5)