nltk.cluster.EMClusterer¶

class nltk.cluster.EMClusterer(initial_means, priors=None, covariance_matrices=None, conv_threshold=1e-06, bias=0.1, normalise=False, svd_dimensions=None)[source]¶

The Gaussian EM clusterer models the vectors as being produced by a mixture of k Gaussian sources. The parameters of these sources (prior probability, mean and covariance matrix) are then found to maximise the likelihood of the given data. This is done with the expectation maximisation algorithm. It starts with k arbitrarily chosen means, priors and covariance matrices. It then calculates the membership probabilities for each vector in each of the clusters; this is the ‘E’ step. The cluster parameters are then updated in the ‘M’ step using the maximum likelihood estimate from the cluster membership probabilities. This process continues until the likelihood of the data does not significantly increase.