  Pairwise relational information is a useful way of providing partial
supervision in domains where class labels are difficult to acquire. This work
presents a clustering model that incorporates pairwise annotations in the form
of must-link and cannot-link relations and considers possible annotation
inaccuracies (i.e., a common setting when experts provide pairwise
supervision). We propose a generative model that assumes Gaussian-distributed
data samples along with must-link and cannot-link relations generated by
stochastic block models. We adopt a maximum-likelihood approach and demonstrate
that, even when supervision is weak and inaccurate, accounting for relational
information significantly improves clustering performance. Relational
information also helps to detect meaningful groups in real-world datasets that
do not fit the original data-distribution assumptions. Additionally, we extend
the model to integrate prior knowledge of experts' accuracy and discuss
circumstances in which the use of this knowledge is beneficial.
