# Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions

arXiv: https://arxiv.org/abs/2303.00522 · HTML: https://mlsanity.com/paper/2303.00522/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 450 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 2 of 2)

- [Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis](https://mlsanity.com/paper/1606.04316/) (2016)
- [Clustering With Side Information: From a Probabilistic Model to a Deterministic Algorithm](https://mlsanity.com/paper/1508.06235/) (2015)

## Built on by (most cited first, 8 of 8)

- [A Survey of the Self Supervised Learning Mechanisms for Vision Transformers](https://mlsanity.com/paper/2408.17059/) (2024)
- [A3S: A General Active Clustering Method with Pairwise Constraints](https://mlsanity.com/paper/2407.10196/) (2024)
- [Angular Constraint Embedding via SpherePair Loss for Constrained Clustering](https://mlsanity.com/paper/2510.06907/) (2025)
- [IPBC: An Interactive Projection-Based Framework for Human-in-the-Loop Semi-Supervised Clustering of High-Dimensional Data](https://mlsanity.com/paper/2601.18828/) (2026)
- [Semi-Supervised Hyperbolic Hierarchical Clustering with Set-Level Structural Priors](https://mlsanity.com/paper/2606.01525/) (2026)
- [Seed-Guided Semi-Supervised Clustering by A-Contrario Anomaly Detection](https://mlsanity.com/paper/2606.18833/) (2026)
- [Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision](https://mlsanity.com/paper/2608.12027/) (2026)
- [An Algebraic Approach to Constrained K-means Clustering](https://mlsanity.com/paper/2610.14932/) (2026)

## Abstract (arXiv)

Clustering is a well-known unsupervised machine learning approach capable of automatically grouping discrete sets of instances with similar characteristics. Constrained clustering is a semi-supervised extension to this process that can be used when expert knowledge is available to indicate constraints that can be exploited. Well-known examples of such constraints are must-link (indicating that two instances belong to the same group) and cannot-link (two instances definitely do not belong together). The research area of constrained clustering has grown significantly over the years with a large variety of new algorithms and more advanced types of constraints being proposed. However, no unifying overview is available to easily understand the wide variety of available methods, constraints and benchmarks. To remedy this, this study presents in-detail the background of constrained clustering and provides a novel ranked taxonomy of the types of constraints that can be used in constrained clustering. In addition, it focuses on the instance-level pairwise constraints, and gives an overview of its applications and its historical context. Finally, it presents a statistical analysis covering 307 constrained clustering methods, categorizes them according to their features, and provides a ranking score indicating which methods have the most potential based on their popularity and validation quality. Finally, based upon this analysis, potential pitfalls and future research directions are provided.