The technological progress of the last decades has made a huge amount
of information available, often expressed in unconventional formats. Among these,
three-way data occur in di®erent application domains from the simultaneous ob-
servation of various attributes on a set of units in di®erent situations or locations.
These include data coming from longitudinal studies of multiple responses, spatio-
temporal data or data collecting multivariate repeated measures. In this work we
propose model based clustering for the wide class of continuous three-way data by
a general mixture model which can be adapted to the di®erent kinds of three-way
data. In so doing we also provide a tool for simultaneously performing model
estimation and model selection. The e®ectiveness of the proposed method is illus-
trated on a simulation study and on real examples.