A Model-based Semi-Supervised Clustering Methodology

TL;DR

This paper extends model-based clustering to semi-supervised scenarios with pre-labeled data, using BIC for model selection, and demonstrates its effectiveness through simulations and biological data analysis.

Contribution

It introduces a BIC-based approach for selecting the number of clusters and relevant variables in semi-supervised model-based clustering.

Findings

Effective in simulation studies

Successfully applied to biological data

Improves clustering accuracy with partial labels

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.