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.
In the expectation-maximization algorithm, we estimate the statistics as if complete data were available, taking into account the distribution of missing data patterns.