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A Framework for Deep Constrained Clustering -- Algorithms and Advances

arXiv 1901.10061 ↗First posted 29 Jan 2019cs.LG stat.MLcited by 24

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Abstract (arXiv)

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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2019.

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Citing papers per year: 2021: 8; 2022: 5; 2023: 3; 2024: 3; 2025: 4; 2026: 1.

24 papers.

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  1. 2022Deep Clustering: A Comprehensive Surveycited by 78
  2. 2023Large Language Models Enable Few-Shot Clusteringcited by 72
  3. 2023ClusterLLM: Large Language Models as a Guide for Text Clusteringcited by 69
  4. 2022Scalable Spectral Clustering with Group Fairness Constraintscited by 16
  5. 2021A Framework for Deep Constrained Clusteringcited by 14
  6. 2021Deep Conditional Gaussian Mixture Model for Constrained Clusteringcited by 14
  7. 2021An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clusteringcited by 12
  8. 2021Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learningcited by 7
  9. 2024TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Datacited by 7
  10. 2021Deep Descriptive Clusteringcited by 5
  11. 2021RepBin: Constraint-based Graph Representation Learning for Metagenomic Binningcited by 5
  12. 2023Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approachcited by 5
12 more
  1. 2024Personalized Clustering via Targeted Representation Learningcited by 4
  2. 2022Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learnerscited by 2
  3. 2025Angular Constraint Embedding via SpherePair Loss for Constrained Clusteringcited by 2
  4. 2021DICE: Deep Significance Clustering for Outcome-Aware Stratificationcited by 1
  5. 2022Spatiotemporal Classification with limited labels using Constrained Clustering for large datasetscited by 1
  6. 2025Enhancing Martian Terrain Recognition with Deep Constrained Clusteringcited by 1
  7. 2025ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptationcited by 1
  8. 2021Constrained Mean Shift for Representation Learningcited by 0
  9. 2022A Bibliographic View on Constrained Clusteringcited by 0
  10. 2024Text-Based Product Matching -- Semi-Supervised Clustering Approachcited by 0
  11. 2025Controllable Conversational Theme Detection Track at DSTC 12cited by 0
  12. 2026Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervisioncited by 0