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.

1 2 3 |

`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` |
an integer, maximum number of iterations of |

`initial.segmentations` |
a list of vectors, segmentations that user wants to be
used as an initial segmentation in |

`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 |

`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)
``` |

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