Tootfinder

Learning-Augmented Hierarchical Clustering
Vladimir Braverman, Jon C. Ergun, Chen Wang, Samson Zhou
https://arxiv.org/abs/2506.05495 https://

Learning-Augmented Hierarchical Clustering
Hierarchical clustering (HC) is an important data analysis technique in which the goal is to recursively partition a dataset into a tree-like structure while grouping together similar data points at each level of granularity. Unfortunately, for many of the proposed HC objectives, there exist strong barriers to approximation algorithms with the hardness of approximation. Thus, we consider the problem of hierarchical clustering given auxiliary information from natural oracles. Specifically, we fo…

UVIT Study of the Magellanic Clouds (U-SMAC). III. Hierarchical Star Formation in the Small Magellanic Cloud Regulated by Turbulence
Sipra Hota, Richard de Grijs, Annapurni Subramaniam
https://arxiv.org/abs/2506.08951

UVIT Study of the Magellanic Clouds (U-SMAC). III. Hierarchical Star Formation in the Small Magellanic Cloud Regulated by Turbulence
The Small Magellanic Cloud (SMC), a satellite galaxy of the Milky Way, is an irregular dwarf galaxy exhibiting evidence of recent and ongoing star formation. We performed a spatial clustering analysis of far-ultraviolet stars in the SMC younger than 150 Myr using data from the Ultra Violet Imaging Telescope onboard AstroSat. We identified 236 young stellar structures as surface overdensities at different significance levels. The sizes of these structures range from a few parsecs to several hund…

Hierarchical community detection via maximum entropy partitions and the renormalization group
Jorge Martinez Armas
https://arxiv.org/abs/2508.04034 https://

Hierarchical community detection via maximum entropy partitions and the renormalization group
Identifying meaningful structure across multiple scales remains a central challenge in network science. We introduce Hierarchical Clustering Entropy (HCE), a general and model-agnostic framework for detecting informative levels in hierarchical community structures. Unlike existing approaches, HCE operates directly on dendrograms without relying on edge-level statistics. It selects resolution levels that maximize a principled trade-off between the entropy of the community size distribution and t…

Network Hexagons Under Attack: Secure Crowdsourcing of Geo-Referenced Data
Okemawo Obadofin, Joao Barros
https://arxiv.org/abs/2506.05601 https://

Network Hexagons Under Attack: Secure Crowdsourcing of Geo-Referenced Data
A critical requirement for modern-day Intelligent Transportation Systems (ITS) is the ability to collect geo-referenced data from connected vehicles and mobile devices in a safe, secure and anonymous way. The Nexagon protocol, which builds on the IETF Locator/ID Separation Protocol (LISP) and the Hierarchical Hexagonal Clustering (H3) geo-spatial indexing system, offers a promising framework for dynamic, privacy-preserving data aggregation. Seeking to address the critical security and privacy v…

Study of AP Association and Users and Power Allocation for Cell-Free Massive MIMO Systems
S. Mohammadzadeh, S. Mashdour, R. C. de Lamare, K. Cumanan, C. Li
https://arxiv.org/abs/2507.03848

Study of AP Association and Users and Power Allocation for Cell-Free Massive MIMO Systems
This paper introduces an access point-user (AP-UE) association strategy combined with pilot power allocation to mitigate multiuser interference and enhance spectral efficiency (SE) in clustered cell-free massive MIMO (CCF-mMIMO) networks. We propose a dynamic channel-based clustering method that groups APs according to their channel correlation, ensuring users are associated with APs exhibiting similar channel characteristics. The proposed approach exploits hierarchical clustering, enabling fle…

Finsler Metric Clustering in Weighted Projective Spaces
Tony Shaska
https://arxiv.org/abs/2507.00001 https://arxiv.org/pdf/2507.00001…

Finsler Metric Clustering in Weighted Projective Spaces
This paper develops a hierarchical clustering algorithm for weighted projective spaces $\mathbb{P}_{\mathbf{q}}$, utilizing a Finsler metric $ d_F([z], [w]) $ and its rational analogue $ d_{F,\mathbb{Q}}([z], [w])$ to define distances that preserve the non-Euclidean geometry of these quotient manifolds. Defined via geodesic integrals of a scaling invariant Finsler norm weighted by the grades $\mathbf{q} = (q_0, q_1, \dots, q_n)$, these metrics satisfy true metric properties including the triang…

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
Zhuocheng Liu, Zhishu Shen, Qiushi Zheng, Tiehua Zhang, Zheng Lei, Jiong Jin
https://arxiv.org/abs/2507.22339

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
Low Earth Orbit (LEO) satellites are emerging as key components of 6G networks, with many already deployed to support large-scale Earth observation and sensing related tasks. Federated Learning (FL) presents a promising paradigm for enabling distributed intelligence in these resource-constrained and dynamic environments. However, achieving reliable convergence, while minimizing both processing time and energy consumption, remains a substantial challenge, particularly in heterogeneous and partia…

Meteoroid stream identification with HDBSCAN unsupervised clustering algorithm
Eloy Pe\~na-Asensio, Fabio Ferrari
https://arxiv.org/abs/2507.01501 https://…

Meteoroid stream identification with HDBSCAN unsupervised clustering algorithm
Accurate identification of meteoroid streams is central to understanding their origins and evolution. However, overlapping clusters and background noise hinder classification, an issue amplified for missions such as ESA's LUMIO that rely on meteor shower observations to infer lunar meteoroid impact parameters. This study evaluates the performance of the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm for unsupervised meteoroid stream identification, …

Parallel Hierarchical Agglomerative Clustering in Low Dimensions
MohammadHossein Bateni, Laxman Dhulipala, Willem Fletcher, Kishen N Gowda, D Ellis Hershkowitz, Rajesh Jayaram, Jakub {\L}\k{a}cki
https://arxiv.org/abs/2507.20047

Parallel Hierarchical Agglomerative Clustering in Low Dimensions
Hierarchical Agglomerative Clustering (HAC) is an extensively studied and widely used method for hierarchical clustering in $\mathbb{R}^k$ based on repeatedly merging the closest pair of clusters according to an input linkage function $d$. Highly parallel (i.e., NC) algorithms are known for $(1+ε)$-approximate HAC (where near-minimum rather than minimum pairs are merged) for certain linkage functions that monotonically increase as merges are performed. However, no such algorithms are known for…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering
Sacha Sokoloski, Philipp Berens

Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese Regulations
Chia-Heng Yu, Yen-Lung Tsai
https://arxiv.org/abs/2506.13607

Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese Regulations
Traditional Retrieval-Augmented Generation (RAG) systems employ brute-force inner product search to retrieve the top-k most similar documents, then combined with the user query and passed to a language model. This allows the model to access external knowledge and reduce hallucinations. However, selecting an appropriate k value remains a significant challenge in practical applications: a small k may fail to retrieve sufficient information, while a large k can introduce excessive and irrelevant c…

Discovery of 63 New Young Asteroid Families
David Nesvorny, David Vokrouhlicky, Miroslav Broz, Fernando V. Roig
https://arxiv.org/abs/2508.00753 https://ar…

Discovery of 63 New Young Asteroid Families
We searched for young asteroid families -- those with ages t_age < 10 Myr and at least three members -- using the proper element catalog from Nesvorny et al. (2024). Our approach employed the Hierarchical Clustering Method (HCM) in a five-dimensional space of proper orbital elements: semimajor axis, eccentricity, inclination, proper nodal longitude, and proper perihelion longitude. The proper longitudes were calculated for various times in the past. Any convergence of these angles at times t < …

Fully-Dynamic Parallel Algorithms for Single-Linkage Clustering
Quinten De Man, Laxman Dhulipala, Kishen N Gowda
https://arxiv.org/abs/2506.18384 https://

Fully-Dynamic Parallel Algorithms for Single-Linkage Clustering
Single-linkage clustering is a popular form of hierarchical agglomerative clustering (HAC) where the distance between two clusters is defined as the minimum distance between any pair of points across the two clusters. In single-linkage HAC, the output is typically the single-linkage dendrogram (SLD), which is the binary tree representing the hierarchy of clusters formed by iteratively contracting the two closest clusters. In the dynamic setting, prior work has only studied maintaining a minimum…

MicroTrace: A Lightweight R Tool for SNP-Based Pathogen Clustering in Outbreak Detection
Kaitao Lai
https://arxiv.org/abs/2507.08060 https://

MicroTrace: A Lightweight R Tool for SNP-Based Pathogen Clustering in Outbreak Detection
MicroTrace is an open-source R tool that performs SNP-based hierarchical clustering to detect potential transmission clusters from pathogen whole-genome sequencing (WGS) data. Designed for epidemiologists, microbiologists, and genomic surveillance teams, it processes SNP distance matrices and outputs dendrograms and cluster tables with optional metadata integration. MicroTrace enables reproducible outbreak detection workflows with minimal setup.