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: http://github.com/blueocean92.

研究人员广泛地探索了约束聚类的领域，并由实践者使用。对于k-means，混合模型和频谱聚类等流行算法，存在约束聚类公式，但有一些限制。深度学习的基本优势是它的灵活性，在这里我们探索一种用于约束聚类的深度学习框架，尤其是探索它如何扩展约束聚类的领域。我们表明，我们的框架不仅可以处理从带标签的附带信息生成的标准共同/单独约束（没有早先记录的负面证据，而且还可以处理从新型附带信息（例如连续值和高级领域知识）生成的更复杂的约束。此外，我们提出了一种有效的训练范例，该范例通常适用于这四种约束。我们通过在图像和文本数据集上的经验结果验证了我们方法的有效性。我们还研究了在嘈杂的约束条件下学习时框架的鲁棒性，并展示了框架的不同组成部分如何影响最终性能。我们的源代码位于：http://github.com/blueocean92。