mlsanityData to 7 Oct 2026

Local algorithms for interactive clustering

arXiv 1312.6724 ↗First posted 24 Dec 2013cs.DS cs.LGcited by 14

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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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Dec 2013.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 4; 2023: 2; 2024: 1; 2025: 1; 2026: 1.

14 papers.

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  1. 2023Large Language Models Enable Few-Shot Clusteringcited by 72
  2. 2021Learning-Augmented k-means Clusteringcited by 26
  3. 2021Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasetscited by 11
  4. 2022Improved Learning-augmented Algorithms for k-means and k-medians Clusteringcited by 10
  5. 2021Active Learning of Driving Scenario Trajectoriescited by 7
  6. 2022Interactive Machine Learning: A State of the Art Reviewcited by 6
  7. 2023Correlation Clustering with Active Learning of Pairwise Similaritiescited by 5
  8. 2021Active Structure Learning of Bayesian Networks in an Observational Settingcited by 4
  9. 2024Information-Theoretic Active Correlation Clusteringcited by 1
  10. 2025ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptationcited by 1
  11. 2021Distribution free optimality intervals for clusteringcited by 0
  12. 2022A Unified Analysis of Dynamic Interactive Learningcited by 0
2 more
  1. 2022A Bibliographic View on Constrained Clusteringcited by 0
  2. 2026Constrained user-item allocation for e-commerce marketing campaignscited by 0