mlcc.reps: Subspace clustering assuming that the number of clusters is...
In psobczyk/public_varclust: Variable Clustering

Description Usage Arguments Details Value Examples

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

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mlcc.reps(X, numb.clusters = 2, numb.runs = 20, stop.criterion = 1,
  max.iter = 20, initial.segmentations = NULL, max.dim = 4,
  scale = TRUE, numb.cores = NULL, estimate.dimensions = TRUE)

`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`	an integer, maximum number of iterations of `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, dimension of subspaces (all are assumed to be equal)
`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

In more detail, an algorithm mlcc.kmeans is run a numb.runs of times with random 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 `cluster.BIC` criterion
`basis`	a list of matrices, the basis vectors for subspaces

1 2	sim.data <- data.simulation(n = 100, SNR = 1, K = 5, numb.vars = 30, max.dim = 2) mlcc.reps(sim.data$X, numb.clusters = 5, numb.runs = 20, max.dim = 4)