Deep Clustering

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Deep clustering studies how to group unlabeled data while learning representations that make meaningful groups easier to separate. Common approaches combine autoencoders or neural encoders with clustering objectives, contrastive learning, prototype assignments, and graph-based methods; some use pretrained image, text, or language representations. These methods support analysis across images, text, graphs, tabular records, time series, and scientific data, including settings with privacy, fairness, or changing data distributions. A central tension is that deep methods can capture complex structure, yet strong conventional clustering on pretrained embeddings can match or exceed them, making robust evaluation, efficiency, and reliable cluster-count selection important open challenges.

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25 papers6 papers in the last four weeks, up 50% on the four weeks before. 0.0% of all new papers.

VTRL-Med: Progressive Visual Refinement with Topology-Guided Regularization for Medical Image Clustering
Oct 23, 2026Guangyu Meng, Pengfei Gu, Peixian Liang +3Deep Clustering Medical Image Analysis
Dept. of Computer Science and Engineering, University of Notre Dame · Dept. of Computer Science, The University of Texas Rio Grande Valley · Dept. of IT, Analytics, and Operations, University of Notre Dame
Compactness and Consistency: A Conjoint Framework for Deep Graph Clustering
Oct 8, 2026Wei Ju, Siyu Yi, Kangjie Zheng +6Low-Rank Representation Learning Graph Clustering
College of Computer Science, Sichuan University · College of Mathematics, Sichuan University · Wellcome Sanger Institute +4
LoomSC: Scalable Deep Subspace Clustering with Projector Factorization and Exact Spectral Reduction
Oct 7, 2026Nairouz Mrabah, Youssef Melki, Mohamed Bouguessa +3Clustering Spectral Clustering
École de technologie supérieure (ÉTS), Montréal, QC, Canada · University of Bahrain (UOB), Kingdom of Bahrain · Université du Québec à Montréal (UQAM), Montréal, QC, Canada +1
DeepTopoClustering: Unsupervised Derivation of Surface Process Taxonomy from 4D Point Clouds for Topographic Monitoring
Oct 7, 2026Jiapan Wang, Daan Hulskemper, Mathilde Letard +2Deep Clustering 4D Scene Understanding
Remote Sensing Applications, TUM School of Engineering and Design, Technical University of Munich, Munich, Germany · Department of Geoscience & Remote Sensing, Delft University of Technology, Delft, The Netherlands
Fed-BRDECS: Privacy-Preserving and Heterogeneity-Aware Federated Deep Embedded Clustering
Oct 5, 2026Haemin Park, Diego Klabjan, Martin W. Braun +2Clustering Deep Clustering
Northwestern University · Intel Corporation
VLM4Cluster: Benchmarking Deep Clustering In the Era of Vision-Language Pre-training
Sep 29, 2026Yuanwei Hu, Bo Peng, Yuheng Jia +3Clustering Deep Clustering
University College London · The University of Queensland · Southeast University +2
CAESAR: Clustering via Autonomous Embedding-Space Agglomerative Reorganization
Sep 26, 2026Ilan Bacry, Rémi Devaux, Antoine JardinCluster Number Estimation Clustering
Arlequin AI
Selective Inference for Deep Clustering in Latent Spaces
Sep 23, 2026Eina Mizui, Tomohiro Shiraishi, Shunichi Nishino +1Selective Inference Clustering
Nagoya University · RIKEN
Federated Deep Clustering Networks for High-Dimensional and Heterogeneous Data
Sep 18, 2026Morris Stallmann, Charalampos S. Kouzinopoulos, Marcin Pietrasik +1Deep Clustering Federated Learning
Department of Advanced Computing Sciences (DACS) Maastricht University Maastricht, Netherlands
Beyond Accuracy: Centroid-Guided Contrastive Loss for Structured Fraudulent Job Posting Detection
Sep 18, 2026Syed Ali Ahmed, Malaika Raza, Muhammad Shoaib Siddiqui +1Contrastive Learning Deep Clustering
National University of Computer and Emerging Sciences, Karachi, Pakistan · National University of Computer and Emerging Sciences, Karachi, 75020, Pakistan · Islamic University of Madinah, Madinah, Saudi Arabia +2
Discovering Dual-Origin Slow Wind from Solar Orbiter with Self-Supervised Contrastive Learning
Aug 22, 2026Henry Han, Jorge Yero SalazarSolar Physics Deep Clustering
Department of Computer Science, Baylor University, One Bear Place, Waco, TX 76798, USA
A Deep Learning Model for Spatially Clustered Data via Differentiable Cluster Assignment
Aug 15, 2026Kexuan Li, Weidong MaNonparametric Regression Clustering
Bristol Myers Squibb · University of Pennsylvania, Department of Biostatistics
Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data
Jul 31, 2026Pulock Das, Yina Hou, Md. Kamrozzaman Bhuiyan +1Class-Imbalanced Learning Clustering
Department of Computer Science Tennessee State University Nashville, TN, USA · Enosis Solutions Dhaka, Bangladesh · Department of Computer Science North Carolina A&T State University Greensboro, NC, USA
Converge to Surprise: Evolutionary Self-supervised Image Clustering
Jul 8, 2026Canlin Zhang, Xiuwen LiuUnsupervised Learning Self-Supervised Learning
Independent Researcher · Department of Computer Science Florida State University
Cohort Organized Learning: Clustering Through Agreement
Jun 19, 2026Finn Henry O'Shea, Maria Elena MonzaniUnsupervised Learning Clustering
SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025-7015, USA · Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305-4085 USA
UniFair: A unified fair clustering approach based on separation and compactness
Jun 3, 2026Antonia Karra, Vasiliki Papanikou, Georgios Vardakas +2Clustering Algorithmic Fairness
University of Ioannina, Greece · Archimedes/Athena Research Center, Greece
CLUBench: A Clustering Benchmark
May 28, 2026Feng Xiao, Dazhi Fu, Chris Ding +1Benchmark Design Clustering
The Chinese University of Hong Kong (Shenzhen)
Beyond Statistical Co-occurrence: Unlocking Intrinsic Semantics for Tabular Data Clustering
Apr 13, 2026Mingjie Zhao, Yunfan Zhang, Yiqun Zhang +1Clustering Deep Clustering
Department of Computer Science, Hong Kong Baptist University, Hong Kong, China · Department of Computer Science, Guangdong University of Technology, Guangzhou, China
Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering
Apr 8, 2026Manar D. Samad, Yina Hou, Shrabani GhoshClustering Deep Clustering
Department of Computer Science Tennessee State University Nashville, TN, USA
DDCL: Deep Dual Competitive Learning: A Differentiable End-to-End Framework for Unsupervised Prototype-Based Representation Learning
Apr 2, 2026Giansalvo CirrincioneClustering Deep Clustering
Lab. LTI, Université de Picardie Jules Verne, Amiens, France
Deep Image Clustering Based on Curriculum Learning and Density Information
Mar 31, 2026Haiyang Zheng, Ruilin Zhang, Hongpeng WangDensity-Based Clustering Clustering
Harbin Institute of Technology, Shenzhen, Shenzhen, China · Harbin Institute of Technology, Shenzhen & Peng Cheng Laboratory, Shenzhen, China
TDEC: Deep Embedded Image Clustering with Transformer and Distribution Information
Mar 23, 2026Ruilin Zhang, Haiyang Zheng, Hongpeng WangClustering Image Clustering
Harbin Institute of Technology, Shenzhen, Shenzhen, China · Harbin Institute of Technology, Shenzhen, & Peng Cheng Laboratory, Shenzhen, China
From Representation to Clusters: A Contrastive Learning Approach for Attributed Hypergraph Clustering
Mar 10, 2026Li Ni, Shuaikang Zeng, Lin Mu +1Hypergraph Learning Deep Clustering
School of Computer Science and Technology, Anhui University, Hefei, China · School of Computer Science and Technology, Southwest University, Chongqing, China
Hypernetwork-based approach for grid-independent functional data clustering
Feb 26, 2026Anirudh Thatipelli, Ali SiahkoohiClustering Deep Clustering
Department of Computer Science, University of Central Florida
Khatri-Rao Clustering for Data Summarization
Feb 17, 2026Martino Ciaperoni, Collin Leiber, Aristides Gionis +1Clustering Deep Clustering
Scuola Normale Superiore Pisa, Italy · Aalto University Espoo, Finland · KTH Royal Institute of Technology Digital Futures Stockholm, Sweden

Oct 23, 2026J/K move · Enter open · S save

VTRL-Med: Progressive Visual Refinement with Topology-Guided Regularization for Medical Image Clustering

Guangyu Meng, Pengfei Gu, Peixian Liang, John P. Lalor+2

Dept. of Computer Science and Engineering, University of Notre Dame · Dept. of Computer Science, The University of Texas Rio Grande Valley · Dept. of IT, Analytics, and Operations, University of Notre Dame

Deep Clustering Medical Image Analysis Medical Image Representation Learning

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Deep clustering offers a promising approach for analyzing large unlabeled medical image datasets, yet prevailing methods still face two limitations: they may neglect class-agnostic structural priors that capture object boundaries and regions, and they do not explicitly encode topological patterns (e.g., connectivity and holes). These sources of information play distinct and complementary roles: visual-structural features provide the primary discriminative representation for clustering, while topological features provide additional global geometric cues for residual visual ambiguities. We introduce VTRL-Med, a progressive visual-structural learning framework with topology-guided regularization for medical image clustering. VTRL-Med first adapts the Segment Anything Model (SAM) to extract image features that retain structural cues inherited from mask-supervised pretraining. It then generates reliable pseudo-labels through a two-step filtering process that combines prototype-based selection with k-nearest neighbor consistency validation. Finally, it progressively refines the visual branch while an auxiliary topology branch, constructed from persistent homology (PH) descriptors, provides additional geometric regularization during training. Across 13 medical image benchmarks, VTRL-Med achieves state-of-the-art clustering performance, improving average accuracy by 4.48% over existing methods. On three representative datasets, topology-guided regularization further improves accuracy by 1.3--4.3% over visual refinement alone.