# A3S: A General Active Clustering Method with Pairwise Constraints

arXiv: https://arxiv.org/abs/2407.10196 · HTML: https://mlsanity.com/paper/2407.10196/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 14 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [SSSNET: Semi-Supervised Signed Network Clustering](https://mlsanity.com/paper/2110.06623/) (2021)
- [k-means++: few more steps yield constant approximation](https://mlsanity.com/paper/2002.07784/) (2020)
- [COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints](https://mlsanity.com/paper/1801.09955/) (2018)
- [Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions](https://mlsanity.com/paper/2303.00522/) (2023)
- [ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities](https://mlsanity.com/paper/1809.06189/) (2018)
- [Query-augmented Active Metric Learning](https://mlsanity.com/paper/2111.04871/) (2021)
- [A semi-supervised sparse K-Means algorithm](https://mlsanity.com/paper/2003.06973/) (2020)
- [Active clustering for labeling training data](https://mlsanity.com/paper/2110.14521/) (2021)
- [Self-Learning Symmetric Multi-view Probabilistic Clustering](https://mlsanity.com/paper/2305.07307/) (2023)

## Built on by (most cited first, 3 of 3)

- [Less is More: Improving LLM Alignment via Preference Data Selection](https://mlsanity.com/paper/2502.14560/) (2025)
- [Optimized Algorithms for Text Clustering with LLM-Generated Constraints](https://mlsanity.com/paper/2601.11118/) (2026)
- [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)

## Abstract (arXiv)

Active clustering aims to boost the clustering performance by integrating human-annotated pairwise constraints through strategic querying. Conventional approaches with semi-supervised clustering schemes encounter high query costs when applied to large datasets with numerous classes. To address these limitations, we propose a novel Adaptive Active Aggregation and Splitting (A3S) framework, falling within the cluster-adjustment scheme in active clustering. A3S features strategic active clustering adjustment on the initial cluster result, which is obtained by an adaptive clustering algorithm. In particular, our cluster adjustment is inspired by the quantitative analysis of Normalized mutual information gain under the information theory framework and can provably improve the clustering quality. The proposed A3S framework significantly elevates the performance and scalability of active clustering. In extensive experiments across diverse real-world datasets, A3S achieves desired results with significantly fewer human queries compared with existing methods.