mlcc.reps: Multiple Latent Components Clustering - Subspace clustering...
In varclust: Variables Clustering

Description Usage Arguments Details Value Examples

For a fixed number of cluster function returns the best partition and basis for each subspace.

mlcc.reps(X, numb.clusters = 2, numb.runs = 30, stop.criterion = 1,
  max.iter = 30, initial.segmentations = NULL, max.dim = 4,
  scale = TRUE, numb.cores = NULL, estimate.dimensions = TRUE,
  flat.prior = FALSE, show.warnings = FALSE)

`X`	A data frame or a matrix with only continuous variables.
`numb.clusters`	An integer, number of cluster.
`numb.runs`	An integer, number of runs of `mlcc.kmeans algorithm` with random initialization.
`stop.criterion`	An integer, if an iteration of `mlcc.kmeans` algorithm makes less changes in partitions than `stop.criterion`, `mlcc.kmeans` stops.
`max.iter`	max.iter An integer, maximum number of iterations of the loop in `mlcc.kmeans` algorithm.
`initial.segmentations`	A list of vectors, segmentations that user wants to be used as an initial segmentation in `mlcc.kmeans` algorithm.
`max.dim`	An integer, maximal dimension of subspaces.
`scale`	A boolean, if TRUE (value set by default) then variables in dataset are scaled to zero mean and unit variance.
`numb.cores`	An integer, number of cores to be used, by default all cores are used.
`estimate.dimensions`	A boolean, if TRUE (value set by default) subspaces dimensions are estimated.
`flat.prior`	A boolean, if TRUE then, instead of a prior that takes into account number of models for a given number of clusters, flat prior is used.
`show.warnings`	A boolean, if set to TRUE all warnings are displayed, default value is FALSE.

In more detail, an algorithm mlcc.kmeans is run a numb.runs of times with random or custom initializations. The best partition is selected according to the BIC.

A list consisting of

`segmentation`	a vector containing the partition of the variables
`BIC`	a numeric, value of the mBIC
`basis`	a list of matrices, the factors for each of the subspaces

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sim.data <- data.simulation(n = 50, SNR = 1, K = 5, numb.vars = 50, max.dim = 3)
mlcc.res <- mlcc.reps(sim.data$X, numb.clusters = 5, numb.runs = 20, max.dim = 4, numb.cores = 1)
show.clusters(sim.data$X, mlcc.res$segmentation)