A Framework for Deep Constrained Clustering -- Algorithms and Advances

2019/01/29 by Hongjing Zhang, Sugato Basu, Zhang, Hongjing +3 · 3 citations
Computer Science · #Advanced Clustering Algorithms Research #Advanced Image and Video Retrieval Techniques #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1901.10061

openalex publication_date 2019/01/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Gradient-based learning applied to document recognition
• Neural network-based clustering using pairwise constraints
• Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering

Cited by

• ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation
• Angular Constraint Embedding via SpherePair Loss for Constrained Clustering
• Controllable Conversational Theme Detection Track at DSTC 12

Related

• A Framework for Deep Constrained Clustering
• Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions
• Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation
• A Bibliographic View on Constrained Clustering
• Deep Conditional Gaussian Mixture Model for Constrained Clustering