klic (version 1.0.4)

lmkkmeans_missingData: Localised multiple kernel k-means

Description

Perform the training step of the localised multiple kernel k-means.

Usage

lmkkmeans_missingData(Km, parameters, missing = NULL, verbose = FALSE)

Arguments

Km

Array of size N X N X M containing M different N x N kernel matrices.

parameters

A list of parameters containing the desired number of clusters, cluster_count, and the number of iterations of the algorithm to be run, iteration_count.

missing

Matrix of size N X M containing missingness indicators, i.e. missing[i,j] = 1 (or = TRUE) if observation i is missing in dataset j, missing[i,j] = 0 (or = FALSE).

verbose

Boolean flag. If TRUE, at each iteration the iteration number is printed. Defaults to FALSE.

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.

Theta

N x M matrix of weights, each row corresponds to an observation and each column to one of the kernels.

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 {
if(requireNamespace("Rmosek", quietly = TRUE) &&
(!is.null(utils::packageDescription("Rmosek")$Configured.MSK_VERSION))){

# Intialise 100 x 100 x 3 array containing M kernel matrices
# representing three different types of similarities between 100 data points
km <- array(NA, c(100, 100, 3))
# Load kernel matrices
km[,,1] <- as.matrix(read.csv(system.file('extdata',
'kernel_matrix1.csv', package = 'klic'), row.names = 1))
km[,,2] <- as.matrix(read.csv(system.file('extdata',
'kernel_matrix2.csv', package = 'klic'), row.names = 1))
km[,,3] <- as.matrix(read.csv(system.file('extdata',
'kernel_matrix3.csv', package = 'klic'), row.names = 1))
# Introduce some missing data
km[76:80, , 1] <- NA
km[, 76:80, 1] <- NA

# Define missingness indicators
missing <- matrix(FALSE, 100, 3)
missing[76:80,1] <- TRUE

# Initalize the parameters of the algorithm
parameters <- list()
# Set the number of clusters
parameters$cluster_count <- 4
# Set the number of iterations
parameters$iteration_count <- 10

# Perform training
state <- lmkkmeans_missingData(km, parameters, missing)

# Display the clustering
print(state$clustering)
# Display the kernel weights
print(state$Theta)
}
# }