# A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints

arXiv: https://arxiv.org/abs/2001.06720 · HTML: https://mlsanity.com/paper/2001.06720/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2020.

## Built on by (most cited first, 10 of 10)

- [Deep Conditional Gaussian Mixture Model for Constrained Clustering](https://mlsanity.com/paper/2106.06385/) (2021)
- [A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering](https://mlsanity.com/paper/2106.16209/) (2021)
- [Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions](https://mlsanity.com/paper/2105.05079/) (2021)
- [Towards Sharper Utility Bounds for Differentially Private Pairwise Learning](https://mlsanity.com/paper/2105.03033/) (2021)
- [ConstraintMatch for Semi-constrained Clustering](https://mlsanity.com/paper/2311.15395/) (2023)
- [Angular Constraint Embedding via SpherePair Loss for Constrained Clustering](https://mlsanity.com/paper/2510.06907/) (2025)
- [A Semi-Supervised Deep Clustering Pipeline for Mining Intentions From Texts](https://mlsanity.com/paper/2202.00802/) (2022)
- [A Bibliographic View on Constrained Clustering](https://mlsanity.com/paper/2209.11125/) (2022)
- [CLiMB: A Domain-Informed Novelty Detection Clustering Framework for Galactic Archaeology and Scientific Discovery](https://mlsanity.com/paper/2601.09768/) (2026)
- [Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision](https://mlsanity.com/paper/2608.12027/) (2026)

## Abstract (arXiv)

In this paper, we introduce a neural network framework for semi-supervised clustering (SSC) with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose SSC into two simpler classification tasks/stages: the first stage uses a pair of Siamese neural networks to label the unlabeled pairs of points as must-link or cannot-link; the second stage uses the fully pairwise-labeled dataset produced by the first stage in a supervised neural-network-based clustering method. The proposed approach, S3C2 (Semi-Supervised Siamese Classifiers for Clustering), is motivated by the observation that binary classification (such as assigning pairwise relations) is usually easier than multi-class clustering with partial supervision. On the other hand, being classification-based, our method solves only well-defined classification problems, rather than less well specified clustering tasks. Extensive experiments on various datasets demonstrate the high performance of the proposed method.