  A mixture of multivariate contaminated normal (MCN) distributions is a useful
model-based clustering technique to accommodate data sets with mild outliers.
However, this model only works when fitted to complete data sets, which is
often not the case in real applications. In this paper, we develop a framework
for fitting a mixture of MCN distributions to incomplete data sets, i.e. data
sets with some values missing at random. We employ the expectation-conditional
maximization algorithm for parameter estimation. We use a simulation study to
compare the results of our model and a mixture of Student's t distributions for
incomplete data.
