The expectation-maximization algorithm is an approach in which values of the statistics which would be computed if a complete dataset were available are estimated (imputed), taking into account the pattern of missing data.
When using the expectation-maximization algorithm, it is assumed that all data would be present for calculating statistics. The method then makes estimates based on the pattern of missing information.