  Analysis of three-way data is becoming ever more prevalent in the literature,
especially in the area of clustering and classification. Real data, including
real three-way data, are often contaminated by potential outlying observations.
Their detection, as well as the development of robust models insensitive to
their presence, is particularly important for this type of data because of the
practical issues concerning their effective visualization. Herein, the
contaminated matrix variate normal distribution is discussed and then utilized
in the mixture model paradigm for clustering. One key advantage of the proposed
model is the ability to automatically detect potential outlying matrices by
computing their \textit{a posteriori} probability to be a "good" or "bad"
point. Such detection is currently unavailable using existing matrix variate
methods. An expectation conditional maximization algorithm is used for
parameter estimation, and both simulated and real data are used for
illustration.
