In this example the GMM clustering method is used to cluster a generated
data set. In GMM clustering one tries to partition n observations into k
clusters in which each observation belongs to the cluster defined by a Gaussian
distribution, whose probability of generating the observation is highest.
The algorithm class constructor takes the number of clusters and a type of
covariance as input. The covariance used in this example is the full covariance.
The training method used in this example is Expectation-Maximization. It takes
minimum covariance, maximum iterations and minimum log-likelihood change as
input. After training one can cluster observations by selecting the most
likely cluster to have generated the observation.

