Clustering

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Clustering seeks useful groupings when labels are absent or incomplete, helping reveal structure in data such as text, images, graphs, time series, and scientific measurements. Methods include centroid-based k-means, density and hierarchical clustering, graph and spectral methods, mixture models, and newer approaches that learn embeddings or use optimal transport; fairness, privacy, streaming, and federated settings add further constraints. Work also studies how to choose the number of clusters and assess stability, uncertainty, interpretability, and statistical significance. No single method works best across data types: deep methods do not consistently beat strong conventional algorithms, and reliable validation remains a central challenge.

Latest papers

347 papers33 papers in the last four weeks, up 18% on the four weeks before. 0.2% of all new papers.

Simple KNN-Based Outlier Detection Achieves Robust Clustering
May 8, 2026Tianle Jiang, Yufa ZhouClustering
Duke University
Classification Fields: Arbitrarily Fine Recursive Hierarchical Clustering From Few Examples
May 8, 2026Yicen Li, Ruiyang Hong, Anastasis Kratsios +2Hierarchical Classification Clustering
Department of Mathematics and Statistics, McMaster University, Canada · Vector Institute, Canada · University of Cambridge, United Kingdom
Drawing Lines in Psychological Space: What K-means Clustering Reveals in Simulated and Real Psychometric Data
May 7, 2026Pedro Henrique Ramos Pinto, Maria Jullyanna Ferreira Marques, Luiz Carlos Serramo LopezClustering
Postgraduate Program in Cognitive and Behavioral Neuroscience (PPGNeC) - Federal University of Paraíba (UFPB) - João Pessoa, PB - Brazil · Center for Health Sciences (CCS) - Federal University of Paraíba (UFPB) - João Pessoa, PB - Brazil
Gaussian mixture models in Hilbert spaces via kernel methods
May 7, 2026Daniel López-Montero, Antonio Álvarez-López, Marcos MatabuenaKernel Mean Embeddings Functional Data Analysis
Friedrich-Alexander-Universität Erlangen-Nürnberg · Universidad Autónoma de Madrid · Mohamed bin Zayed University of Artificial Intelligence
Jointly Learning Structured Representations and Stabilized Affinity for Human Motion Segmentation
May 7, 2026Xianghan Meng, Zhiyuan Huang, Zhengyu Tong +1Representation Learning Clustering
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, P.R. China
Scalable inference of spatial regions and temporal signatures from time series
May 6, 2026Jiayu Weng, Alec KirkleyClustering Time Series Clustering
Institute of Data Science, University of Hong Kong, Hong Kong SAR, China · School of Computing and Data Science, University of Hong Kong, Hong Kong SAR, China · Department of Urban Planning and Design, University of Hong Kong, Hong Kong SAR, China +1
Gradient Flow Structure and Quantitative Dynamics of Multi-Head Self-Attention
May 5, 2026Ayan PendharkarSoftmax Attention Attention Head Analysis
AdaGraph: A Graph-Native Clustering Algorithm That Overcomes the Curse of Dimensionality and Enables Scientific Discovery
May 5, 2026Ahmed ElmahdiClustering Graph Clustering
Independent Researcher
Towards Robust and Scalable Density-based Clustering via Graph Propagation
May 1, 2026Yingtao Zheng, Hugo Phibbs, Ninh PhamGraph-Based Label Propagation Clustering
School of Computer Science, University of Auckland, New Zealand. · Department of Mathematics and Computer Science, University of Southern Denmark, Denmark.
Efficient Multivector Retrieval with Token-Aware Clustering and Hierarchical Indexing
Apr 30, 2026Silvio Martinico, Franco Maria Nardini, Cosimo Rulli +1Clustering Vector Quantization
University of Pisa & ISTI–CNR · Pisa, Italy · ISTI–CNR +1
ClimateVID -- Social Media Videos Analysis and Challenges Involved
Apr 30, 2026Shiqi Xu, Moritz Burmester, Katharina Prasse +3VLM Evaluation Social Media Analysis
University of Mannheim · Technical University of Munich · University of Mannheim, Max-Planck-Institute for Informatics, Saarland Informatics Campus
Physically-Informed Fuzzy Clustering of Vertical Sounding Ionograms
Apr 30, 2026Oleg I. Berngardt, Sergey N. PonomarchukClustering Remote Sensing
Characterizing Admissible Objective Functions for Hierarchical Clustering
Apr 26, 2026Ryuki Tsukuba, Kazutoshi AndoClustering Hierarchical Clustering
Graduate School of Integrated Science and Technology, Shizuoka University,Jun Hamamatsu, Shizuoka 432-8561, Japan · Faculty of Engineering, Shizuoka University, Johoku 3-5-1, 432-8561 Hamamatsu, Japan
Explainable AI in Speaker Recognition -- Making Latent Representations Understandable
Apr 25, 2026Yanze Xu, Wenwu Wang, Mark D. PlumbleyClustering Hierarchical Clustering
Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, UK · Department of Informatics, King’s College London, London, WC2R 2LS, UK
Turtle shell clustering: A mixture approach to discriminative clustering with applications to flow cytometry and other data
Apr 25, 2026Mackenzie R. Neal, Paul D. McNicholas, Arthur WhiteClustering Gaussian Mixture Models
Department of Mathematics & Statistics, McMaster University, Ontario, Canada · School of Computer Science & Statistics, Trinity College Dublin, Ireland
Associativity-Peakiness Metric for Contingency Tables
Apr 24, 2026Naomi E. Zirkind, William J. DiehlClustering
Army Research Directorate, DEVCOM Army Research Laboratory, 2800 Powder Mill Road, Adelphi, 20783, MD, USA.
How Hard Is Continuous Clustering? Lower Bounds from the Existential Theory of the Reals
Apr 24, 2026Angshul MajumdarClustering Density-Based Clustering
IIIT Delhi
Robust Fuzzy local k-plane clustering with mixture distance of hinge loss and L1 norm
Apr 24, 2026Junjun Huang, Xiliang Lu, Xuelin Xie +1Clustering
China Electric Power Research Institute, Wuhan 430074, China, and also with the Key Laboratory of Measurement and Test of High Voltage and Heavy Current, State Administration for Market Regulation, Wuhan 430074, China · School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China, the National Center for Applied Mathematics in Hubei, Wuhan University, Wuhan 430072, China, and also with the Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan 430072, China · National Center for Applied Mathematics in Hubei, Wuhan University, Wuhan 430072, China, the Wuhan Institute for Math and AI, Wuhan University, Wuhan 430072, China, the School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China, and the Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan 430072, China
Assessing the impact of dimensionality reduction on clustering performance - a systematic study
Apr 23, 2026Ousmane Assani Amate, Elyes Lounissi, Mohammadreza Bakhtyari +3Principal Component Analysis Clustering
Université du Québec à Montréal, Montreal, Quebec, Canada · Mila - Quebec AI Institute, Montreal, Quebec, Canada
An effective variant of the Hartigan $k$ -means algorithm
Apr 23, 2026François Clément, Stefan SteinerbergerClustering Unsupervised Clustering
FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning
Apr 21, 2026Abdulmoneam Ali, Ahmed ArafaClustering Noisy-Label Learning
Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, NC 28223, USA
Deep Sprite-based Image Models: An Analysis
Apr 21, 2026Zeynep Sonat Baltacı, Romain Loiseau, Mathieu AubryClustering Object-Centric Representation Learning
LIGM, CNRS, Univ Gustave Eiffel, ENPC, Institut Polytechnique de Paris, France
An Adaptive Spatiotemporal Clustering Framework for 3D Ocean Subsurface Temperature Reconstruction
Apr 21, 2026Ming Shan Loo, Wengen Li, Xudong Jiang +4Clustering
School of Computer Science and Technology, Tongji University, Shanghai, China · The Key Laboratory of Road and Traffic Engineering, Ministry of Education, College of Transportation, Tongji University, Shanghai, China · Project Management Office of China National Scientific Seafloor Observatory, Tongji University, Shanghai, China
Preserving Clusters in Error-Bounded Lossy Compression of Particle Data
Apr 20, 2026Congrong Ren, Sheng Di, Katrin Heitmann +2Clustering
The Ohio State University · Argonne National Laboratory
Clusterability-Based Assessment of Potentially Noisy Views for Multi-View Clustering
Apr 20, 2026Mudi Jiang, Jiahui Zhou, Xinying Liu +2Clustering Multi-View Learning
School of Software, Dalian University of Technology, Dalian, China · School of Software, Dalian University of Technology, Dalian, China, and Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China
Constant-Factor Approximations for Doubly Constrained Fair k-Center, k-Median and k-Means
Apr 17, 2026Nicole Funk, Annika Hennes, Johanna Hillebrand +1Clustering Group Fairness
Department of Informatics, University of Cologne, Germany · Heinrich Heine University Düsseldorf, Faculty of Mathematics and Natural Sciences, Germany · Department of Informatics, University of Bonn, Germany
Why Colors Make Clustering Harder:Global Integrality Gaps, the Price of Fairness, and Color-Coupled Algorithms in Chromatic Correlation Clustering
Apr 17, 2026Ibne Farabi Shihab, Sanjeda Akter, Anuj SharmaClustering Approximation Algorithms
Department of Computer Science, Iowa State University · Department of Civil, Construction & Environmental Engineering, Iowa State University
Scalable Model-Based Clustering with Sequential Monte Carlo
Apr 16, 2026Connie Trojan, Pavel Myshkov, Paul Fearnhead +3Clustering
Lancaster University · Microsoft Research
Composite Silhouette: A Subsampling-based Aggregation Strategy
Apr 15, 2026Aggelos Semoglou, Aristidis Likas, John PavlopoulosCluster Number Estimation Clustering
Athens University of Economics and Business, Greece. · Archimedes, Athena Research Center, Greece. · University of Ioannina, Greece.
Deep Spatially-Regularized and Superpixel-Based Diffusion Learning for Unsupervised Hyperspectral Image Clustering
Apr 14, 2026Vutichart Buranasiri, James M. MurphySuperpixel Segmentation Clustering
Department of Mathematics, Tufts University, Medford, MA 02155, USA

May 8, 2026J/K move · Enter open · S save

Simple KNN-Based Outlier Detection Achieves Robust Clustering

Tianle Jiang, Yufa Zhou

Duke University

Clustering

Open paper AskPDF arXiv

Being robust to the presence of outliers is crucial for applying clustering algorithms in practice. In the

\textit{robust

-Means}

problem (i.e.,

k

-Means with outliers), the goal is to remove

z

outliers and minimize the

k

-Means cost on the remaining points. Despite the close connection between robust

k

-Means and outlier detection, both theoretical and empirical understanding of the effectiveness of

\textit{classic outlier detection heuristics}

for robust

k

-Means remains limited. In this paper, we prove that under a practical assumption on the optimal cluster sizes, simply removing points with large

K

-Nearest-Neighbor distances achieves performance comparable to prior work in terms of approximation guarantees: it yields a constant-factor reduction from robust

k

-Means to standard

k

-Means, without introducing additional centers or discarding extra outliers, as is commonly required by existing approaches. Empirically, experiments on real-world datasets show that our method outperforms or matches several more sophisticated algorithms in terms of clustering cost and runtime. These results demonstrate that simple KNN-based heuristics can be surprisingly effective for robust clustering, highlighting new opportunities to bridge techniques from outlier detection and clustering.