# A Framework for Deep Constrained Clustering -- Algorithms and Advances

arXiv: https://arxiv.org/abs/1901.10061 · HTML: https://mlsanity.com/paper/1901.10061/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2019.

## Built on by (most cited first, 24 of 24)

- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [Large Language Models Enable Few-Shot Clustering](https://mlsanity.com/paper/2307.00524/) (2023)
- [ClusterLLM: Large Language Models as a Guide for Text Clustering](https://mlsanity.com/paper/2305.14871/) (2023)
- [Scalable Spectral Clustering with Group Fairness Constraints](https://mlsanity.com/paper/2210.16435/) (2022)
- [A Framework for Deep Constrained Clustering](https://mlsanity.com/paper/2101.02792/) (2021)
- [Deep Conditional Gaussian Mixture Model for Constrained Clustering](https://mlsanity.com/paper/2106.06385/) (2021)
- [An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clustering](https://mlsanity.com/paper/2111.15571/) (2021)
- [Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning](https://mlsanity.com/paper/2112.04607/) (2021)
- [TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data](https://mlsanity.com/paper/2406.10061/) (2024)
- [Deep Descriptive Clustering](https://mlsanity.com/paper/2105.11549/) (2021)
- [RepBin: Constraint-based Graph Representation Learning for Metagenomic Binning](https://mlsanity.com/paper/2112.11696/) (2021)
- [Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach](https://mlsanity.com/paper/2305.19391/) (2023)
- [Personalized Clustering via Targeted Representation Learning](https://mlsanity.com/paper/2412.13690/) (2024)
- [Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners](https://mlsanity.com/paper/2202.01153/) (2022)
- [Angular Constraint Embedding via SpherePair Loss for Constrained Clustering](https://mlsanity.com/paper/2510.06907/) (2025)
- [DICE: Deep Significance Clustering for Outcome-Aware Stratification](https://mlsanity.com/paper/2101.02344/) (2021)
- [Spatiotemporal Classification with limited labels using Constrained Clustering for large datasets](https://mlsanity.com/paper/2210.07522/) (2022)
- [Enhancing Martian Terrain Recognition with Deep Constrained Clustering](https://mlsanity.com/paper/2503.17633/) (2025)
- [ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation](https://mlsanity.com/paper/2512.04350/) (2025)
- [Constrained Mean Shift for Representation Learning](https://mlsanity.com/paper/2110.10309/) (2021)
- [A Bibliographic View on Constrained Clustering](https://mlsanity.com/paper/2209.11125/) (2022)
- [Text-Based Product Matching -- Semi-Supervised Clustering Approach](https://mlsanity.com/paper/2402.10091/) (2024)
- [Controllable Conversational Theme Detection Track at DSTC 12](https://mlsanity.com/paper/2508.18783/) (2025)
- [Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision](https://mlsanity.com/paper/2608.12027/) (2026)

## 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.