# Local algorithms for interactive clustering

arXiv: https://arxiv.org/abs/1312.6724 · HTML: https://mlsanity.com/paper/1312.6724/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2013.

## Built on by (most cited first, 14 of 14)

- [Large Language Models Enable Few-Shot Clustering](https://mlsanity.com/paper/2307.00524/) (2023)
- [Learning-Augmented k-means Clustering](https://mlsanity.com/paper/2110.14094/) (2021)
- [Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets](https://mlsanity.com/paper/2105.11653/) (2021)
- [Improved Learning-augmented Algorithms for k-means and k-medians Clustering](https://mlsanity.com/paper/2210.17028/) (2022)
- [Active Learning of Driving Scenario Trajectories](https://mlsanity.com/paper/2108.03217/) (2021)
- [Interactive Machine Learning: A State of the Art Review](https://mlsanity.com/paper/2207.06196/) (2022)
- [Correlation Clustering with Active Learning of Pairwise Similarities](https://mlsanity.com/paper/2302.10295/) (2023)
- [Active Structure Learning of Bayesian Networks in an Observational Setting](https://mlsanity.com/paper/2103.13796/) (2021)
- [Information-Theoretic Active Correlation Clustering](https://mlsanity.com/paper/2402.03587/) (2024)
- [ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation](https://mlsanity.com/paper/2512.04350/) (2025)
- [Distribution free optimality intervals for clustering](https://mlsanity.com/paper/2107.14442/) (2021)
- [A Unified Analysis of Dynamic Interactive Learning](https://mlsanity.com/paper/2204.07071/) (2022)
- [A Bibliographic View on Constrained Clustering](https://mlsanity.com/paper/2209.11125/) (2022)
- [Constrained user-item allocation for e-commerce marketing campaigns](https://mlsanity.com/paper/2606.09623/) (2026)

## Abstract (arXiv)

We study the design of interactive clustering algorithms for data sets satisfying natural stability assumptions. Our algorithms start with any initial clustering and only make local changes in each step; both are desirable features in many applications. We show that in this constrained setting one can still design provably efficient algorithms that produce accurate clusterings. We also show that our algorithms perform well on real-world data.