A Framework for Deep Constrained Clustering -- Algorithms and Advances
This work addresses the problem of constrained clustering for practitioners by offering a more flexible and robust approach, though it appears incremental as it builds on existing deep learning and constrained clustering concepts.
The authors tackled the limitations of existing constrained clustering methods by introducing a deep learning framework that handles standard and complex constraints from various types of side information, showing it avoids negative effects and extends the field.
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.