Clustering with Quantitative User Preferences on Attributes

This paper proposes a new semi-supervised clustering framework to represent and integrate quantitative preferences on attributes. A new metric learning algorithm is derived that achieves a compromise clustering between a data-driven and a user-driven solution and converges with a good complexity. We observe experimentally that the addition of preferences may be essential to achieve a better clustering. We also show that our approach performs better than the state-of-the art algorithms.