Description Usage Arguments Value Examples

`MBM.cluster`

calculates a model based clustering using multivariate Bernoulli-mixtures as probabilistic model of the data.
The quality of the clustering is judged using the AIC criterion.

1 | ```
MBM.cluster(data, min = 1, max = 10)
``` |

`data` |
A numeric matrix. |

`min` |
The minimal number of components that is tested. |

`max` |
The maximal number of components that is tested. |

A list with 3 elements. The first element is the minimal AIC value for each tested number of components.
The second element is a vector of all AIC values. The third is the actual clustering as returned by the EM algorithm using
the optimal number of components according to AIC. The element is again a list that contains the mixture coefficients, the actual
parameters of the mutlivariate Benroulli distributions, the probability matrix of each observation (i.e. row if `data`

)
and component and the number of iterations that the EM algorithm needed to converge.

1 2 3 4 5 6 7 8 9 10 | ```
#Random data generation, 100 observations, 5 dimensions, dependencies within the dimensions
data = cbind(round(runif(100)), round(runif(100)), round(runif(100)))
data = cbind(data, data[,2], 1-data[,3])
#Noisy data:
s = round(runif(2, 1, 100))
data[s, c(4,5)] = 1 - data[s, c(4,5)]
#MBMM Clustering
res = MBM.cluster(data, 1,8)
``` |

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