  We consider an extension of model-based clustering to the semi-supervised
case, where some of the data are pre-labeled. We provide a derivation of the
Bayesian Information Criterion (BIC) approximation to the Bayes factor in this
setting. We then use the BIC to the select number of clusters and the variables
useful for clustering. We demonstrate the efficacy of this adaptation of the
model-based clustering paradigm through two simulation examples and a fly
larvae behavioral dataset in which lines of neurons are clustered into
behavioral groups.
