  The area of constrained clustering has been extensively explored by
researchers and used by practitioners. Constrained clustering formulations
exist for popular algorithms such as k-means, mixture models, and spectral
clustering but have several limitations. A fundamental strength of deep
learning is its flexibility, and here we explore a deep learning framework for
constrained clustering and in particular explore how it can extend the field of
constrained clustering. We show that our framework can not only handle standard
together/apart constraints (without the well documented negative effects
reported earlier) generated from labeled side information but more complex
constraints generated from new types of side information such as continuous
values and high-level domain knowledge. Furthermore, we propose an efficient
training paradigm that is generally applicable to these four types of
constraints. We validate the effectiveness of our approach by empirical results
on both image and text datasets. We also study the robustness of our framework
when learning with noisy constraints and show how different components of our
framework contribute to the final performance. Our source code is available at
$\href{https://github.com/blueocean92/deep_constrained_clustering}{\text{URL}}$.
