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.
A numeric matrix.
The minimal number of components that is tested.
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
and component and the number of iterations that the EM algorithm needed to converge.
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#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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