Constrained Clustering Using Gaussian Processes

Constrained clustering is an important machine learning, signal processing and data mining tool, for discovering clusters in data, in the presence of additional domain information. The present work introduces a probabilistic scheme for constrained clustering based on the popular Gaussian Process framework. The proposed scheme accommodates pairwise, must- and cannot-link constraints between data, does not require hyperparameter tuning, and enables assessment of the reliability of obtained results. Preliminary results on real data showcase the potential of the proposed approach.