A Model-based Semi-Supervised Clustering Methodology

Abstract: 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.