Clustering

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Overview

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

CardsList

345 papers31 papers in the last four weeks, up 11% on the four weeks before. 0.2% of all new papers.

  1. Melodic contour does not cluster: Reconsidering contour typology

    Apr 13, 2026Bas Cornelissen, Willem Zuidema, John Ashley Burgoyne +1Clustering

    Institute for Logic, Language and Computation, University of Amsterdam

  2. Distributionally Robust K-Means Clustering

    Apr 13, 2026Vikrant Malik, Taylan Kargin, Babak HassibiClusteringDistribution Shift Robustness

    Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125 USA. · California Institute of Technology, Pasadena, CA 91125 USA · Massachusetts Institute of Technology, Cambridge, MA 02139 USA.

  3. Beyond Statistical Co-occurrence: Unlocking Intrinsic Semantics for Tabular Data Clustering

    Apr 13, 2026Mingjie Zhao, Yunfan Zhang, Yiqun Zhang +1ClusteringDeep Clustering

    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China · Department of Computer Science, Guangdong University of Technology, Guangzhou, China

  4. A Structured Clustering Approach for Inducing Media Narratives

    Apr 11, 2026Rohan Das, Advait Deshmukh, Alexandria Leto +3ClusteringComputational Social Science

    University of Colorado Boulder · Independent Researcher

  5. The Condition-Number Principle for Prototype Clustering

    Apr 9, 2026Romano Li, Jianfei CaoClustering

    Department of Economics, Duke University · Department of Economics, Northeastern University

  6. On the Uphill Battle of Image frequency Analysis

    Apr 8, 2026Nader Bazyari, Hedieh SajediFrequency-Domain Image ProcessingImage Clustering

    Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran

  7. Reasoning-Based Refinement of Unsupervised Text Clusters with LLMs

    Apr 8, 2026Tunazzina IslamClusteringUnsupervised Clustering

    Department of Computer Science, Purdue University, West Lafayette, IN 47907

  8. Lumbermark: Resistant Clustering by Chopping Up Mutual Reachability Minimum Spanning Trees

    Apr 8, 2026Marek GagolewskiDensity-Based ClusteringClustering

    Warsaw University of Technology, Faculty of Mathematics and Information Science, ul. Koszykowa 75, 00-662 Warsaw, Poland · Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland

  9. Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering

    Apr 8, 2026Manar D. Samad, Yina Hou, Shrabani GhoshClusteringDeep Clustering

    Department of Computer Science Tennessee State University Nashville, TN, USA

  10. A Data-Informed Variational Clustering Framework for Noisy High-Dimensional Data

    Apr 8, 2026Wan Ping ChenClusteringUnsupervised Clustering

    Department of Mathematics, Fu Jen Catholic University, Taipei, Taiwan, No. 510 Zhongzheng Rd., Xinzhuang Dist., New Taipei City, 242062, Taiwan

  11. Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data

    Apr 7, 2026Lehao Li, Qiang Huang, Yihao Ang +3Clustering

    School of Computing, National University of Singapore, Singapore · School of Intelligence Science and Engineering, Harbin Institute of Technology (Shenzhen), China

  12. Individual-heterogeneous sub-Gaussian Mixture Models

    Apr 7, 2026Huan QingClusteringGaussian Mixture Models

    School of Economics and Finance, Chongqing University of Technology, Chongqing, 400054, China

  13. CavMerge: Merging K-means Based on Local Log-Concavity

    Apr 5, 2026Zhili Qiao, Wangqian Ju, Peng LiuClusteringUnsupervised Clustering

    Department of Statistics, Iowa State University

  14. Biconvex Biclustering

    Apr 5, 2026Sam Rosen, Eric C. Chi, Jason XuClusteringFeature Selection

    Department of Statistical Science, Duke University · School of Statistics, University of Minnesota · Department of Biostatistics, University of California Los Angeles

  15. PRISM: LLM-Guided Semantic Clustering for High-Precision Topics

    Apr 3, 2026Connor Douglas, Utkucan Balci, Joseph Aylett-BullockSemantic ClusteringTopic Modeling

    New York University New York, NY, USA · Binghamton University Binghamton, NY, USA · United Nations New York, NY, USA

  16. A Novel Theoretical Analysis for Clustering Heteroscedastic Gaussian Data without Knowledge of the Number of Clusters

    Apr 2, 2026Dominique Pastor, Elsa Dupraz, Ismail Hbilou +1ClusteringUnsupervised Clustering

    Lab-STICC, IMT Atlantique, 29238 Brest, France · OPTI’SEA 23 rue du Bourgneuf 29300 Quimperlé, France

  17. DDCL: Deep Dual Competitive Learning: A Differentiable End-to-End Framework for Unsupervised Prototype-Based Representation Learning

    Apr 2, 2026Giansalvo CirrincioneClusteringDeep Clustering

    Lab. LTI, Université de Picardie Jules Verne, Amiens, France

  18. Deep Image Clustering Based on Curriculum Learning and Density Information

    Mar 31, 2026Haiyang Zheng, Ruilin Zhang, Hongpeng WangDensity-Based ClusteringClustering

    Harbin Institute of Technology, Shenzhen, Shenzhen, China · Harbin Institute of Technology, Shenzhen & Peng Cheng Laboratory, Shenzhen, China

  19. Kempe Swap K-Means: A Scalable Near-Optimal Solution for Semi-Supervised Clustering

    Mar 28, 2026Yuxuan Ren, Shijie DengClusteringSemi-Supervised Learning

    School of Industrial and Systems Engineering Georgia Institute of Technology, USA

  20. Cluster-Adaptive Feature Extraction and its Theoretical Foundation with Minkowski Weighted k-Means

    Mar 26, 2026Renato Cordeiro de Amorim, Vladimir MakarenkovClusteringUnsupervised Clustering

    School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe, UK. · D´epartement d’informatique, Universit´e du Qu´ebec `a Montr´eal, C.P. 8888 succ. Centre-Ville, Montreal (QC) H3C 3P8 Canada. · Mila - Quebec AI Institute, Montreal, QC, Canada.

  21. Language-Assisted Image Clustering Guided by Discriminative Relational Signals and Adaptive Semantic Centers

    Mar 25, 2026Jun Ma, Xu Zhang, Zhengxing Jiao +4Image ClusteringClustering

    School of Computer Science and Engineering, Southeast University, Nanjing, China · School of Computing Information Sciences, Saint Francis University, Hong Kong, China · Department of Computer Science, City University of Hong Kong, Hong Kong, China

  22. Granular Ball Guided Stable Latent Domain Discovery for Domain-General Crowd Counting

    Mar 25, 2026Fan Chen, Shuyin Xia, Yi WangCrowd CountingDomain Generalization

    Chongqing University of Posts and Telecommunications, Chongqing, China · Chongqing Ant Consumer Finance Co., Ltd., Chongqing, China

  23. Contrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectors

    Mar 24, 2026Max Marriott-Clarke, Lazar Novakovic, Elizabeth Ratzer +3Density-Based Clustering3D Point Cloud Segmentation

    Blackett Laboratory, Imperial College London, UK · Department of Physics & Astronomy, Brown University, USA · Brown Center for Theoretical Physics and Innovation (BCTPI), Brown University, USA

  24. The Mass Agreement Score: A Point-centric Measure of Cluster Size Consistency

    Mar 24, 2026Randolph Wiredu-AidooClustering

  25. TDEC: Deep Embedded Image Clustering with Transformer and Distribution Information

    Mar 23, 2026Ruilin Zhang, Haiyang Zheng, Hongpeng WangClusteringImage Clustering

    Harbin Institute of Technology, Shenzhen, Shenzhen, China · Harbin Institute of Technology, Shenzhen, & Peng Cheng Laboratory, Shenzhen, China

  26. CNMBI: Determining the Number of Clusters Using Center Pairwise Matching and Boundary Filtering

    Mar 23, 2026Ruilin Zhang, Haiyang Zheng, Hongpeng WangCluster Number EstimationClustering

    Harbin Institute of Technology, Shenzhen, Shenzhen, China · Peng Cheng Laboratory, Shenzhen, China

  27. Feature Incremental Clustering with Generalization Bounds

    Mar 23, 2026Jing Zhang, Chenping HouClusteringGeneralization Bounds

    Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education), School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan 410081, P. R. China. · College of Liberal Arts and Sciences, National University of Defense Technology, Changsha, Hunan, 410073, China.

  28. Distributed Gradient Clustering: Convergence and the Effect of Initialization

    Mar 20, 2026Aleksandar Armacki, Himkant Sharma, Dragana Bajović +3ClusteringDistributed Optimization

    Carnegie Mellon University, Pittsburgh, Pennsylvania, USA · Indian Institute of Technology Kharagpur, Kharagpur, India · Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia +1

  29. A Super Fast K-means for Indexing Vector Embeddings

    Mar 20, 2026Leonardo Kuffo, Sven Hepkema, Peter BonczClustering

    CWI, Amsterdam, The Netherlands · Systems Group, ETH Zurich, Zurich, Switzerland

  30. Explainable cluster analysis: a bagging approach

    Mar 20, 2026Federico Maria Quetti, Elena Ballante, Silvia Figini +1ClusteringUnsupervised Clustering

    Department of Mathematics, University of Pavia, Pavia, 27100, Italy. · Department of Social and Political Sciences, University of Pavia, Pavia, 27100, Italy. · Department of Economics and Management, University of Pavia, Pavia, 27100, Italy.