Research map / Learning theory and algorithms
Clustering: research map
725 accepted papers on Clustering in Learning theory and algorithms, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 9 approaches. The busiest year so far is 2026.
Within Learning theory and algorithms, its share grew from 6.3% in 2023–24 to 8.5% in 2025–26 (140 → 206 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
Explore Clustering in the interactive map
Working on something in this topic? Describe your idea in scime atlas to see which approach it falls under, the closest papers by meaning and how crowded the spot has become.
Approaches and key papers
correlation clustering · hierarchical · spectral clustering · 541 papers
Approaches in this cluster:
- Clustering approximation algorithms (150 papers)
Design provable algorithms for hierarchical and general clustering objectives, often via convex relaxation. - Correlation clustering (102 papers)
Develop approximation algorithms for correlation and hierarchical clustering on graphs. - Subspace clustering (102 papers)
Cluster data lying near union of subspaces using self-expression and multi-view methods. - Fairness-constrained clustering (19 papers)
Cluster data with demographic fairness constraints using spectral, deep or variational methods. - Incomplete multi-view clustering (125 papers)
Clusters multi-view data with missing views using semantic alignment and contrastive prototype matching. - Spectral clustering algorithms (43 papers)
Make spectral graph clustering efficient and analyze its behavior.
Most cited and most cited since 2024:
- Superpixels and Polygons Using Simple Non-Iterative Clustering (CVPR 2017 · 496 citations)
- Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit (CVPR 2016 · 394 citations)
- Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised Clustering Analysis (CVPR 2024 · 36 citations)
- Low-Rank Kernel Tensor Learning for Incomplete Multi-View Clustering (AAAI 2024 · 35 citations)
center · median · coreset · 184 papers
Approaches in this cluster:
- Coresets and dynamic clustering (77 papers)
Use coresets and dynamic data structures for k-center and robust clustering. - k-means++ seeding analysis (76 papers)
Analyze and speed up k-means++ seeding and Lloyd-type algorithms. - Fair clustering approximation (31 papers)
Approximation algorithms for centroid clustering with demographic fairness, such as fairlets.
Most cited and most cited since 2024:
- q-means: A quantum algorithm for unsupervised machine learning (NeurIPS 2019 · 108 citations)
- Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search (NeurIPS 2017 · 78 citations)
- Parameterized Approximation Algorithms for Sum of Radii Clustering and Variants (AAAI 2024 · 9 citations)
- Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers (AAAI 2024 · 5 citations)
Related topics in Learning theory and algorithms
- Matrix and tensor methods (1,074)
- Sample complexity (1,363)
- Combinatorial and search optimization (1,492)
- Fairness (516)
- Submodular and game algorithms (1,030)
- Prediction and decision losses (2,134)
- Kernels and regression theory (1,004)
- Optimal transport (384)