klic (version 1.0.4)

kkmeans: Kernel k-means

Description

Perform the training step of kernel k-means.

Usage

kkmeans(K, parameters)

Arguments

K

Kernel matrix.

parameters

A list containing the number of clusters number_count.

Value

This function returns a list containing:

clustering

the cluster labels for each element (i.e. row/column) of the kernel matrix.

objective

the value of the objective function for the given clustering.

parameters

same parameters as in the input.

References

Gonen, M. and Margolin, A.A., 2014. Localized data fusion for kernel k-means clustering with application to cancer biology. In Advances in Neural Information Processing Systems (pp. 1305-1313).

Examples

# NOT RUN {
# Load one dataset with 100 observations, 2 variables, 4 clusters
data <- as.matrix(read.csv(system.file("extdata", "dataset1.csv",
package = "klic"), row.names = 1))
# Compute consensus clustering with K=4 clusters
cm <- coca::consensusCluster(data, 4)
# Shift eigenvalues of the matrix by a constant: (min eigenvalue) * (coeff)
km <- spectrumShift(cm, coeff = 1.05)
# Initalize the parameters of the algorithm
parameters <- list()
# Set the number of clusters
parameters$cluster_count <- 4
# Perform training
state <- kkmeans(km, parameters)
# Display the clustering
print(state$clustering)
# }