Model based clustering for three-way data structures

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 different application domains from the simultaneous observation of various attributes on a set of units in different 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 different kinds of three-way data. In so doing we also provide a tool for simultaneously performing model estimation and model selection. The effectiveness of the proposed method is illustrated on a simulation study and on real examples.