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…

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…

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…

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…

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, …

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…

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…

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…