mlcc.kmeans {varclust}

Multiple Latent Components Clustering - kmeans algorithm

Description

Performs k-means based subspace clustering. Center of each cluster is some number of principal components. This number can be fixed or estimated by PESEL. Similarity measure between variable and a cluster is calculated using BIC.

Usage

mlcc.kmeans(X, number.clusters = 2, stop.criterion = 1,
  max.iter = 30, max.subspace.dim = 4, initial.segmentation = NULL,
  estimate.dimensions = TRUE, show.warnings = FALSE)

Arguments

X

A matrix with only continuous variables.

number.clusters

An integer, number of clusters to be used.

stop.criterion

An integer indicating how many changes in partitions triggers stopping the algorithm.

max.iter

An integer, maximum number of iterations of k-means loop.

max.subspace.dim

An integer, maximum dimension of subspaces.

initial.segmentation

A vector, initial segmentation of variables to clusters.

estimate.dimensions

A boolean, if TRUE (value set by default) subspaces dimensions are estimated.

show.warnings

A boolean, if set to TRUE all warnings are displayed, default value is FALSE.

Value

A list consisting of:

segmentation

a vector containing the partition of the variables

pcas

a list of matrices, basis vectors for each cluster (subspace)

References

Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood, Piotr Sobczyk, Malgorzata Bogdan, Julie Josse

Examples

sim.data <- data.simulation(n = 50, SNR = 1, K = 5, numb.vars = 50, max.dim = 3)
mlcc.res <- mlcc.kmeans(sim.data$X, number.clusters = 5, max.iter = 20, max.subspace.dim = 3)
show.clusters(sim.data$X, mlcc.res$segmentation)