The EM (Expectation Maximization) algorithm is used in practice to find the “optimal” parameters of the distributions that maximize the likelihood function.

The number of clusters is a parameter of the algorithm. But we can also detect the “optimal” number of clusters by evaluating several values, i.e. testing 1 cluster, 2 clusters, etc. and choosing the best one (which maximizes the likelihood or another criterion such as AIC or BIC).

**Keywords**: clustering, expectation maximization algorithm, gaussian mixture model

**Components**: EM-Clustering, K-Means, EM-Selection, scatterplot

**Tutorial**: en_Tanagra_EM_Clustering.pdf

**Dataset**: two_gaussians.xls

**Reference**:

Wikipédia (en) -- Expectation-maximization algorithm