# Information-Theoretic Active Correlation Clustering

arXiv: https://arxiv.org/abs/2402.03587 · HTML: https://mlsanity.com/paper/2402.03587/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 68 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning](https://mlsanity.com/paper/2106.12059/) (2021)
- [Clustering with Noisy Queries](https://mlsanity.com/paper/1706.07510/) (2017)
- [Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities](https://mlsanity.com/paper/2208.00549/) (2022)
- [A Survey of Signed Network Mining in Social Media](https://mlsanity.com/paper/1511.07569/) (2015)
- [Query Complexity of Clustering with Side Information](https://mlsanity.com/paper/1706.07719/) (2017)
- [Correlation Clustering with Same-Cluster Queries Bounded by Optimal Cost](https://mlsanity.com/paper/1908.04976/) (2019)
- [Local algorithms for interactive clustering](https://mlsanity.com/paper/1312.6724/) (2013)
- [Active Learning for Sound Event Detection](https://mlsanity.com/paper/2002.05033/) (2020)
- [Approximate Correlation Clustering Using Same-Cluster Queries](https://mlsanity.com/paper/1712.06865/) (2017)
- [COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints](https://mlsanity.com/paper/1801.09955/) (2018)
- [Active Learning with Logged Data](https://mlsanity.com/paper/1802.09069/) (2018)
- [Correlation Clustering with Adaptive Similarity Queries](https://mlsanity.com/paper/1905.11902/) (2019)
- [Query-Efficient Correlation Clustering](https://mlsanity.com/paper/2002.11557/) (2020)
- [Active Learning of Driving Scenario Trajectories](https://mlsanity.com/paper/2108.03217/) (2021)
- [Query-Efficient Correlation Clustering with Noisy Oracle](https://mlsanity.com/paper/2402.01400/) (2024)
- [Local correlation clustering](https://mlsanity.com/paper/1312.5105/) (2013)
- [Correlation Clustering with Active Learning of Pairwise Similarities](https://mlsanity.com/paper/2302.10295/) (2023)
- [Shift of Pairwise Similarities for Data Clustering](https://mlsanity.com/paper/2110.13103/) (2021)
- [Learning Representations from Dendrograms](https://mlsanity.com/paper/1812.09225/) (2018)

## Built on by (most cited first, 1 of 1)

- [An Efficient Local Search Approach for Polarized Community Discovery in Signed Networks](https://mlsanity.com/paper/2502.02197/) (2025)

## Abstract (arXiv)

Correlation clustering is a flexible framework for partitioning data based solely on pairwise similarity or dissimilarity information, without requiring the number of clusters as input. However, in many practical scenarios, these pairwise similarities are not available a priori and must be obtained through costly measurements or human feedback. This motivates the use of active learning to query only the most informative pairwise comparisons, enabling effective clustering under budget constraints. In this work, we develop a principled active learning approach for correlation clustering by introducing several information-theoretic acquisition functions that prioritize queries based on entropy and expected information gain. These strategies aim to reduce uncertainty about the clustering structure as efficiently as possible. We evaluate our methods across a range of synthetic and real-world settings and show that they significantly outperform existing baselines in terms of clustering accuracy and query efficiency. Our results highlight the benefits of combining active learning with correlation clustering in settings where similarity information is costly or limited.