Cluster Number Estimation

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13 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Federated Clustering with Unknown Local and Global Cluster Cardinalities
Sep 29, 2026Mitushi Goyal, Tarun S., Riddhanya Senapathi +1Cluster Number Estimation Clustering
BITS Pilani K. K. Birla Goa Campus, Goa, India.
Focus and Supplement: Dual-Enhanced Vision Transformer for Multi-Class Anomaly Classification
Sep 27, 2026Xurui Li, Enjie Xu, Chenzhou Li +4Cluster Number Estimation Vision Transformer
School of Electronic Information and Communications, Huazhong University of Science and Technology
CAESAR: Clustering via Autonomous Embedding-Space Agglomerative Reorganization
Sep 26, 2026Ilan Bacry, Rémi Devaux, Antoine JardinCluster Number Estimation Clustering
Arlequin AI
Bracketing Uncertainty in Clustering Under the Manifold Hypothesis
Sep 15, 2026Savik Kinger, Luciano Dyballa, Steven W. ZuckerCluster Number Estimation Clustering
Department of Computer Science Yale University · School of Science & Technology IE University · Depts. of Computer Science and Biomedical Engineering Wu Tsai Institute Yale University
Alcmean's: Unsupervised community detection using local Laplacian, automatic detection of the number of centers
Jun 8, 2026Shahin Momenzadeh, Rojiar Pir MohammadianiCluster Number Estimation Clustering
Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran
Central Description Length (CDL) Clustering Validation Index
Jun 2, 2026Mahdi Shamsi, Soosan BeheshtiMinimum Description Length Clustering
Toronto Metropolitan University, 350 Victoria St., Toronto, Ontario M5B 2K3, Canada
Amortized Neural Clustering of Time Series based on Statistical Features
May 13, 2026Ángel López-Oriona, Ying SunCluster Number Estimation Amortized Inference
CEMSE Division, Statistics Program, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia. · Statistics Program, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Fast estimation of Gaussian mixture components via centering and singular value thresholding
Apr 21, 2026Huan QingGaussian Mixture Models Cluster Number Estimation
School of Economics and Finance, Chongqing University of Technology, Chongqing, 400054, Chongqing, China.
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.
CNMBI: Determining the Number of Clusters Using Center Pairwise Matching and Boundary Filtering
Mar 23, 2026Ruilin Zhang, Haiyang Zheng, Hongpeng WangCluster Number Estimation Clustering
Harbin Institute of Technology, Shenzhen, Shenzhen, China · Peng Cheng Laboratory, Shenzhen, China
Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers
Mar 13, 2026Yue Zhang, Chuanlong Qiu, Xinfa Liao +1Cluster Number Estimation Clustering
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China · School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China · Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
The elbow statistic: Multiscale clustering statistical significance
Mar 3, 2026Francisco J. Perez-RecheCluster Number Estimation Clustering
School of Natural and Computing Sciences, University of Aberdeen, Fraser Noble Building, AB24 3UE, United Kingdom
TabClustPFN: A Prior-data Fitted Network for Tabular Data Clustering
Jan 29, 2026Tianqi Zhao, Guanyang Wang, Yan Shuo Tan +1Cluster Number Estimation Clustering
Renmin University of China · Rutgers University · National University of Singapore

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Federated Clustering with Unknown Local and Global Cluster Cardinalities

Mitushi Goyal, Tarun S., Riddhanya Senapathi, Arun Raman

BITS Pilani K. K. Birla Goa Campus, Goa, India.

Cluster Number Estimation Clustering Clustered Federated Learning Gaussian Mixture Models

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Federated clustering methods that do not require the global number of clusters

K

still assume that each client knows its local number

K_g

. This assumption is hard to justify when clients know no more about their data than the server does, as in fault diagnosis across independently operated industrial sites. We propose a two-phase framework in which neither count is known: each client first estimates

K_g

from its own data, and an aggregator that requires local counts, such as FedGEM, then uses these estimates in place of the true values. For the first phase we introduce Adaptive Split--Merge (ASM), which grows a spherical Gaussian mixture by BIC-driven splitting and then merges excess components. ASM uses no labels, selects its hyperparameters on held-out client data only, and makes no assumption about how clusters are shared across clients. We derive a closed-form split criterion whose critical cluster size falls with anisotropy and rises with dimension, and show empirically that over-fragmentation grows with the number of points per cluster, which federation divides among clients. Across eight datasets, ASM with FedGEM attains a mean ARI of 0.333, against 0.256 for the next best label-free estimator and 0.361 when the true local counts are supplied. It also gives the most reliable global estimates of

K

and is robust when client size is decoupled from local cardinality.