Description Usage Arguments Details Value Examples
For a fixed number of cluster function returns the best partition and basis for each subspace.
1 2 3 4 
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 
stop.criterion 
An integer, if an iteration of

max.iter 
max.iter An integer, maximum number of iterations of the loop
in 
initial.segmentations 
A list of vectors, segmentations that user wants
to be used as an initial segmentation in 
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 
1 2 3  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)

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