Hierarchical Clustering

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4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 19

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Algorithmically Aligned Neural Agglomerative Tree Construction
Oct 5, 2026Robert R Nerem, Pranav Singh, Cheyenne Ward +1Clustering Hierarchical Clustering
University of California San Diego · University of Washington
Round-Trip KNN Clustering: multiscale hierarchical cluster detection on directed nearest-neighbour graphs
Oct 5, 2026Eraldo Pereira Marinho, Caetano Mazzoni Ranieri, Fabricio Aparecido BreveClustering Hierarchical Clustering
Department of Statistics, Applied Mathematics and Computing, S˜ao Paulo State University (UNESP), Institute of Geosciences and Exact Sciences, Av. 24-A, 1515, Rio Claro, S˜ao Paulo, Brazil.
Interrelating Fruchterman-Reingold Graph Visualization and Agglomerative Clustering
Sep 28, 2026Alexandre Benatti, Luciano da F. CostaScientific Visualization Hierarchical Clustering
Institute of Mathematics and Statistics - DCC University of S˜ao Paulo Rua do Mat˜ao, 1010, S˜ao Paulo, SP 05508-090 Brazil · S˜ao Carlos Institute of Physics - DFCM University of S˜ao Paulo Av. Trabalhador S˜ao-Carlense, 400, S˜ao Carlos, SP 13566-590 Brazil
Hierarchical Clustering and Signal Denoising on Digraphs
Sep 28, 2026Yi Wang, Sippanon Kitimoon, Hrushikesh N. Mhaskar +1Graph Signal Processing Hierarchical Clustering
Department of Mathematics, City University of Hong Kong, Hong Kong, SAR China · Data Science Research Center, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand · Institute of Mathematical Sciences, Claremont Graduate University, Claremont, CA 91711, USA
Automatic depth-based local center clustering via $β$ -integrated local depth and adaptive grouping
Sep 22, 2026Siyi Wang, Alexandre Leblanc, Paul D. McNicholasClustering Hierarchical Clustering
Hierarchical Clustering Can Jointly Satisfy Richness, Consistency, and Scale Invariance
Sep 11, 2026Daichi Kuroda, Maximilien Dreveton, Matthias Grossglauser +1Clustering Hierarchical Clustering
School of Computer and Communication Sciences ´Ecole Polytechnique F´ed´erale de Lausanne (EPFL) Lausanne, 1015, Switzerland · LAMA, UMR-CNRS 8050, Universit´e Gustave Eiffel 5 Bd Descartes, 77454 Marne-la-Vall´ee, France
Density-aware Hierarchical Clustering Based on Element-Categorized Connection Subgraphs
Aug 7, 2026Yuning Yu, José Rodríguez-Piñeiro, Xuefeng Yin +1Clustering Hierarchical Clustering
Tongji University, Shanghai, China · RadioSky (Shanghai) Communication Technology Co., Ltd, Shanghai, China
Graph Partitioning with Demands: Generalized Conductance and its Applications
Jul 14, 2026Michał Szyfelbein, Dariusz DereniowskiHierarchical Clustering Approximation Algorithms
Gda´nsk University of Technology
Hierarchical $\mathcal{F}$ -Clustering: Approximation and Hardness of Clustering into Trees and Bounded Diameter Graphs
Jul 14, 2026Michał Szyfelbein, Dariusz DereniowskiHierarchical Clustering Graph Clustering
Gda´nsk University of Technology
Performance Benchmarking and Optimisation of Clustering Algorithms for Local and Non-Local Similarity Measure in Medical Image Analysis
Jul 10, 2026Sisipho Hamlomo, Marcellin AtemkengMedical Image Analysis Clustering
Department of Mathematics, Rhodes University, PO Box 94, Makhanda, 6140, South Africa · Department of Statistics, Rhodes University, PO Box 94, Makhanda, 6140, South Africa · National Institute for Theoretical and Computational Sciences (NITheCS), Stellenbosch 7600, South Africa
Term-Centric Hierarchy Induction from Heterogeneous Corpora
Jun 25, 2026Elena Senger, Yuri Campbell, Jan-Peter Bergmann +2Clustering Hierarchical Clustering
1MaiNLP, Center for Information and Language Processing, LMU Munich, Germany · 2Fraunhofer Institute for Systems and Innovation Research ISI, Germany · Department of Computer Science, IT University of Copenhagen, Denmark
FOSC-X: An Extended Framework for Optimal Local Cuts and Non-Horizontal Cluster Selection from Clustering Hierarchies
Jun 17, 2026Connor Simpson, Ricardo J. G. B. CampelloDynamic Programming Clustering
Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
Recovering the Zipfian Distribution in Unsupervised Term Discovery
Jun 9, 2026Danel Slabbert, Simon Malan, Herman KamperClustering Hierarchical Clustering
Electrical and Electronic Engineering, Stellenbosch University, South Africa
Semi-Supervised Hyperbolic Hierarchical Clustering with Set-Level Structural Priors
Jun 1, 2026Junjing Zheng, Xinyu Zhang, Xiangfeng Qiu +2Clustering Hierarchical Clustering
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Score-Agnostic Structure Analysis in Large-Scale Performance Datasets
May 25, 2026Patricia Hu, Silvan Peter, Gerhard WidmerHierarchical Clustering Music Information Retrieval
Institute of Computational Perception, Johannes Kepler University, Linz, Austria · LIT AI Lab, Linz Institute of Technology, Linz, Austria
Unsupervised clustering and classification of upper limb EMG signals during functional movements: a data-driven
May 20, 2026L. F. Salazar Álvarez, D. Escobar-Saltarén, M. B. Salazar Sánchez +1EMG-Based Gesture Recognition Clustering
In2Lab, Engineer Faculty, Universidad de Antioquia. Medellín, Calle 70 No. 52-21, A.A. 1226 · School of Engineering and Sciencies, Tecnológico de Monterrey. Guadalajara, México
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
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

Oct 5, 2026, cs.LGJ/K move · Enter open · S save

Algorithmically Aligned Neural Agglomerative Tree Construction

Robert R Nerem, Pranav Singh, Cheyenne Ward, Yusu Wang

University of California San Diego · University of Washington

Clustering Hierarchical Clustering Phylogenetic Inference

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Linkage algorithms for hierarchical clustering (HC) are a powerful and efficient framework for constructing clustering trees, yet it is often unclear which merge rule best suits a given dataset or task. In contrast, neural approaches can learn from data, but often fail to retain the efficiency and size generalization of classical algorithms. We introduce NN-linkage, a neural network (NN) model that can learn task-specific and locally dependent merge rules while retaining the recursive structure and efficient inference of classical linkage algorithms. In particular, our model is algorithmically aligned with the Lance-Williams (LW) recurrence, a parameterized framework for defining a broad, continuous family of linkage rules for agglomerative HC. Classical methods such as single linkage (SL), complete linkage (CL), and average linkage arise as discrete choices within this broader family. We show that NN-linkage is a universal approximator for continuous linkage functions, including LW recurrences, and, when paired with a transformer encoding, can also approximate globally dependent rules such as robust single-linkage. We further show that NN-linkage can exactly implement any symmetric constant-coefficient LW recurrence across all input sizes. On the empirical front, we evaluate NN-linkage in real-world applications, clock-tree routing and phylogenetic reconstruction, using both synthetic and real datasets, demonstrating its effectiveness over both classical algorithms and other neural approaches. By learning merge rules directly from target trees, NN-linkage extends efficient HC to scientific and engineering objectives not adequately captured by existing hand-designed linkage rules.

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