# Clustering-driven Deep Embedding with Pairwise Constraints

arXiv: https://arxiv.org/abs/1803.08457 · HTML: https://mlsanity.com/paper/1803.08457/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2018.

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

- [A Framework for Deep Constrained Clustering](https://mlsanity.com/paper/2101.02792/) (2021)
- [Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos](https://mlsanity.com/paper/2203.13704/) (2022)
- [Deep Descriptive Clustering](https://mlsanity.com/paper/2105.11549/) (2021)
- [Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation](https://mlsanity.com/paper/2203.12546/) (2022)
- [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)
- [DMS: Differentiable Mean Shift for Dataset Agnostic Task Specific Clustering Using Side Information](https://mlsanity.com/paper/2305.18492/) (2023)
- [Learning Multimodal Affinities for Textual Editing in Images](https://mlsanity.com/paper/2103.10139/) (2021)
- [Exploring dual information in distance metric learning for clustering](https://mlsanity.com/paper/2105.12703/) (2021)
- [Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision](https://mlsanity.com/paper/2608.12027/) (2026)

## Abstract (arXiv)

Recently, there has been increasing interest to leverage the competence of neural networks to analyze data. In particular, new clustering methods that employ deep embeddings have been presented. In this paper, we depart from centroid-based models and suggest a new framework, called Clustering-driven deep embedding with PAirwise Constraints (CPAC), for non-parametric clustering using a neural network. We present a clustering-driven embedding based on a Siamese network that encourages pairs of data points to output similar representations in the latent space. Our pair-based model allows augmenting the information with labeled pairs to constitute a semi-supervised framework. Our approach is based on analyzing the losses associated with each pair to refine the set of constraints. We show that clustering performance increases when using this scheme, even with a limited amount of user queries. We demonstrate how our architecture is adapted for various types of data and present the first deep framework to cluster 3D shapes.