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.
@article{arxiv.1901.10061,
title = {A Framework for Deep Constrained Clustering -- Algorithms and Advances},
author = {Hongjing Zhang and Sugato Basu and Ian Davidson},
journal= {arXiv preprint arXiv:1901.10061},
year = {2019}
}