# Constraint-Based Clustering Selection

arXiv: https://arxiv.org/abs/1609.07272 · HTML: https://mlsanity.com/paper/1609.07272/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2016.

## Built on by (most cited first, 6 of 6)

- [A Hybrid Architecture for Out of Domain Intent Detection and Intent Discovery](https://mlsanity.com/paper/2303.04134/) (2023)
- [Towards Explainable Clustering: A Constrained Declarative based Approach](https://mlsanity.com/paper/2403.18101/) (2024)
- [Machine Learning for Missing Value Imputation](https://mlsanity.com/paper/2410.08308/) (2024)
- [Problem-oriented AutoML in Clustering](https://mlsanity.com/paper/2409.16218/) (2024)
- [Applying Semi-Automated Hyperparameter Tuning for Clustering Algorithms](https://mlsanity.com/paper/2108.11053/) (2021)
- [Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering](https://mlsanity.com/paper/2602.18348/) (2026)

## Abstract (arXiv)

Semi-supervised clustering methods incorporate a limited amount of supervision into the clustering process. Typically, this supervision is provided by the user in the form of pairwise constraints. Existing methods use such constraints in one of the following ways: they adapt their clustering procedure, their similarity metric, or both. All of these approaches operate within the scope of individual clustering algorithms. In contrast, we propose to use constraints to choose between clusterings generated by very different unsupervised clustering algorithms, run with different parameter settings. We empirically show that this simple approach often outperforms existing semi-supervised clustering methods.