\input{figures/pseudo-code/em}
The Expectation Maximization algorithm\cite{top10} is an expansion of the standard K-means(\tion{k-means}) algorithm. Rather than using a single distribution to assign instances with continuous and enumerated attributes to a cluster, a combination of different distributions are used. The Expectation Maximization algorithm is an iterative two step process:
\bd
\item[E] The estimation phase redefines the likelihood function with respect to the new parameters.
\item[M] The maximization phase attempts to maximizes the likelihood function in respect to the previous set of parameters.
\ed

The above two steps are repeated until either the parameters converge or a maximum number of iterations is reached.
