Clustering

Follow this topic Ask about this topic

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

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

Scalable unsupervised feature selection via weight stability
Jun 6, 2025Xudong Zhang, Renato Cordeiro de AmorimUnsupervised Learning Clustering
School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe, UK.
Clustering and Pruning in Causal Data Fusion
May 21, 2025Otto Tabell, Santtu Tikka, Juha KarvanenClustering Causal Identifiability
Department of Mathematics and Statistics FI-40014 University of Jyvaskyla, Finland
Clustering with Non-adaptive Subset Queries
Sep 17, 2024Hadley Black, Euiwoong Lee, Arya Mazumdar +1Query Complexity Clustering
CUNY Baruch College · University of Michigan · UC San Diego
Interpretable Clustering: A Survey
Sep 1, 2024Lianyu Hu, Mudi Jiang, Junjie Dong +2Clustering Interpretable ML
College of Information Science and Engineering, Henan University of Technology, China · School of Software, Dalian University of Technology, China · Xinchang Power Supply Company, State Grid Corporation of China, China
Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping
Aug 6, 2024Jinghui Yuan, Chusheng Zeng, Fangyuan Xie +5Clustering Graph Clustering
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China · Institute of Artificial Intelligence (TeleAI), China Telecom, P. R. China
Faster and Simpler Greedy Algorithm for $k$ -Median and $k$ -Means
Jul 15, 2024Max Dupré la Tour, David SaulpicClustering Approximation Algorithms
Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection
Feb 3, 2024Zishi Zhang, Yijie PengClustering
Guanghua School of Management, Peking University, Beijing 100871, China · School of Management and Engineering, Nanjing University, Nanjing 210008, China
Optimal Clustering of Multi-layer Networks under Mixture Block Models
Nov 27, 2023Zhongyuan Lyu, Ting Li, Dong XiaClustering Stochastic Block Model
Department of Mathematics, Hong Kong University of Science and Technology · Department of Applied Mathematics, The Hong Kong Polytechnic University
Clustering Three-Way Data with Outliers
Oct 8, 2023Katharine M. Clark, Paul D. McNicholasClustering
Department of Mathematics & Statistics, Trent University, Ontario, Canada. · Department of Mathematics & Statistics, McMaster University, Ontario, Canada.
Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering
Jun 22, 2023Tianyu Zhao, Junping Du, Yingxia Shao +1Federated Learning Aggregation Clustering
Beijing Key Laboratory of Intelligent Communication Software and Multimedia, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China
Proportionally Representative Clustering
Apr 27, 2023Haris Aziz, Barton E. Lee, Sean Morota Chu +1Clustering Algorithmic Fairness
UNSW Sydney · ETH Zürich · Northwestern University
Anisotropic View Distance Metric for High-Dimensional Data: Theory, Geometry, and Fast Computation
Jun 10, 2022Yiqun Zhang, Hou-biao LiClustering
School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China

Jun 6, 2025J/K move · Enter open · S save

Scalable unsupervised feature selection via weight stability

Xudong Zhang, Renato Cordeiro de Amorim

School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe, UK.

Unsupervised Learning Clustering Unsupervised Clustering Feature Selection

Open paper AskPDF arXiv

Unsupervised feature selection is critical for improving clustering performance in high-dimensional data, where irrelevant features can obscure meaningful structure. In this work, we propose the Minkowski weighted

k

-means++, a novel initialisation strategy for the Minkowski Weighted

k

-means. Our initialisation selects centroids probabilistically using feature relevance estimates derived from the data itself. Building on this, we propose two new feature selection algorithms, FS-MWK++, which aggregates feature weights across a range of Minkowski exponents identifying stable and informative features, and SFS-MWK++, a scalable variant based on subsampling. We support our approach with a theoretical analysis, demonstrating that, under explicit assumptions on noise features and cluster structure, relevant features are assigned consistently higher weights than noise features across a range of Minkowski exponents. Our software can be found at https://github.com/xzhang4-ops1/FSMWK.