Tootfinder

Few-shot Unknown Class Discovery of Hyperspectral Images with Prototype Learning and Clustering
Chun Liu, Chen Zhang, Zhuo Li, Zheng Li, Wei Yang
https://arxiv.org/abs/2508.18075

Few-shot Unknown Class Discovery of Hyperspectral Images with Prototype Learning and Clustering
Open-set few-shot hyperspectral image (HSI) classification aims to classify image pixels by using few labeled pixels per class, where the pixels to be classified may be not all from the classes that have been seen. To address the open-set HSI classification challenge, current methods focus mainly on distinguishing the unknown class samples from the known class samples and rejecting them to increase the accuracy of identifying known class samples. They fails to further identify or discovery the …

Dense Subgraph Clustering and a New Cluster Ensemble Method
The-Anh Vu-Le, Jo\~ao Alfredo Cardoso Lamy, Tom\'as Alessi, Ian Chen, Minhyuk Park, Elfarouk Harb, George Chacko, Tandy Warnow
https://arxiv.org/abs/2508.17013

Dense Subgraph Clustering and a New Cluster Ensemble Method
We propose DSC-Flow-Iter, a new community detection algorithm that is based on iterative extraction of dense subgraphs. Although DSC-Flow-Iter leaves many nodes unclustered, it is competitive with leading methods and has high-precision and low-recall, making it complementary to modularity-based methods that typically have high recall but lower precision. Based on this observation, we introduce a novel cluster ensemble technique that combines DSC-Flow-Iter with modularity-based clustering, to pr…

Estimating the Effective Topics of Articles and journals Abstract Using LDA And K-Means Clustering Algorithm
Shadikur Rahman, Umme Ayman Koana, Aras M. Ismael, Karmand Hussein Abdalla
https://arxiv.org/abs/2508.16046

Estimating the Effective Topics of Articles and journals Abstract Using LDA And K-Means Clustering Algorithm
Analyzing journals and articles abstract text or documents using topic modelling and text clustering has become a modern solution for the increasing number of text documents. Topic modelling and text clustering are both intensely involved tasks that can benefit one another. Text clustering and topic modelling algorithms are used to maintain massive amounts of text documents. In this study, we have used LDA, K-Means cluster and also lexical database WordNet for keyphrases extraction in our text …

Analytic inference with two-way clustering
Laurent Davezies, Xavier D'Haultf{\oe}uille, Yannick Guyonvarch
https://arxiv.org/abs/2506.20749 https://

Analytic inference with two-way clustering
This paper studies analytic inference along two dimensions of clustering. In such setups, the commonly used approach has two drawbacks. First, the corresponding variance estimator is not necessarily positive. Second, inference is invalid in non-Gaussian regimes, namely when the estimator of the parameter of interest is not asymptotically Gaussian. We consider a simple fix that addresses both issues. In Gaussian regimes, the corresponding tests are asymptotically exact and equivalent to usual on…

Addressing Dipole Tension via Clustering in $\Lambda$CDM and beyond
Arefeh Daei Rasouli, Haniyeh S. Tadayyoni, Shant Baghram, Sohrab Rahvar
https://arxiv.org/abs/2508.18259 http…

Addressing Dipole Tension via Clustering in $Λ$CDM and beyond
The dipole in the angular distribution of the cosmic microwave background (CMB) is typically attributed to the Doppler effect and our motion relative to the CMB rest frame. It is expected that observations of large-scale structures would also exhibit a related kinematic dipole, helping to confirm the kinematic origin of the CMB dipole. However, numerous studies of the large-scale structure dipole have shown significant discrepancies with predictions based on the CMB. In this work, we investigat…

Convergence of Mean Shift Algorithms for Large Bandwidths and Simultaneous Accurate Clustering
Susovan Pal, Praneeth Vepakomma
https://arxiv.org/abs/2506.19837

Convergence of Mean Shift Algorithms for Large Bandwidths and Simultaneous Accurate Clustering
The mean shift (MS) is a non-parametric, density-based, iterative algorithm that has prominent usage in clustering and image segmentation. A rigorous proof for its convergence in full generality remains unknown. Two significant steps in this direction were taken in the paper \cite{Gh1}, which proved that for \textit{sufficiently large bandwidth}, the MS algorithm with the Gaussian kernel always converges in any dimension, and also by the same author in \cite{Gh2}, proved that MS always converge…

Clustering of quantum correlations at low temperature
Arka Adhikari, Joscha Henheik, Marius Lemm, Tom Wessel
https://arxiv.org/abs/2508.15907 https://arxiv…

Clustering of quantum correlations at low temperature
Identifying conditions for the clustering of correlations in thermal states is a central problem in quantum many-body physics. In the low temperature regime, the problem is subtle, because it is intimately connected to phase transitions. Here we investigate quantum lattice Hamiltonians that are sums of on-site terms and relatively bounded perturbations. We present a direct proof that the thermal state satisfies exponential decay of correlations at low temperature, with a correlation length that…

Clustering Tails in High Dimension
Liujun Chen, Marco Oesting, Chen Zhou
https://arxiv.org/abs/2506.19414 https://arxiv.org/pdf/2506.…

Clustering Tails in High Dimension
One potential solution to combat the scarcity of tail observations in extreme value analysis is to integrate information from multiple datasets sharing similar tail properties, for instance, a common extreme value index. In other words, for a multivariate dataset, we intend to group dimensions into clusters first, before applying any pooling techniques. This paper addresses the clustering problem for a high dimensional dataset, according to their extreme value indices. We propose an iterative…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/9]:
- Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering
Emmanouil Kritharakis, Dusan Jakovetic, Antonios Makris, Konstantinos Tserpes

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…

What Makes the Best Decomposition? Investigating Binary Decomposition Under FCG Variance
Ang Jia, He Jiang, Zhilei Ren, Xiaochen Li, Ming Fan, Ting Liu
https://arxiv.org/abs/2506.19425

What Makes the Best Decomposition? Investigating Binary Decomposition Under FCG Variance
Binary decomposition, which decomposes binary files into modules, plays a critical role in binary reuse detection. Existing binary decomposition works either apply anchor-based methods by extending anchor functions to generate modules, or apply clustering-based methods by using clustering algorithms to group binary functions, which all rely on that reused code shares similar function call relationships. However, we find that function call graphs (FCGs) vary a lot when using different compilatio…

Replaced article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/2]:
- A Global Optimization Algorithm for K-Center Clustering of One Billion Samples
Jiayang Ren, Ningning You, Kaixun Hua, Chaojie Ji, Yankai Cao

Transformer Based Multi-Target Bernoulli Tracking for Maritime Radar
Caden Sweeney, Du Yong Kim, Branko Ristic, Brian Cheung
https://arxiv.org/abs/2506.20319

Transformer Based Multi-Target Bernoulli Tracking for Maritime Radar
Multi-target tracking in the maritime domain is a challenging problem due to the non-Gaussian and fluctuating characteristics of sea clutter. This article investigates the use of machine learning (ML) to the detection and tracking of low SIR targets in the maritime domain. The proposed method uses a transformer to extract point measurements from range-azimuth maps, before clustering and tracking using the Labelled mulit- Bernoulli (LMB) filter. A measurement driven birth density design based on…

A Unified Lagrangian Framework for Galaxy Clustering: Consistent Modeling of Bias, Redshift-Space Distortions, and Reconstruction
Naonori Sugiyama
https://arxiv.org/abs/2508.17331

A Unified Lagrangian Framework for Galaxy Clustering: Consistent Modeling of Bias, Redshift-Space Distortions, and Reconstruction
We present \emph{Unified Lagrangian Perturbation Theory} (ULPT), a perturbative framework for consistently modeling galaxy density fluctuations across real space, redshift space, and post-reconstruction fields. Unlike existing approaches that treat these cases separately, ULPT provides a single theoretical structure that incorporates the three essential coordinate mappings: the Lagrangian-to-Eulerian transformation, the real-to-redshift mapping induced by peculiar velocities, and the remapping …

Asteroid Rotation Periods: Statistical Analysis in the Diameter-Spin Distribution
Maryam Nastaran, Atila Poro, Raziyeh Hosseini, Matin Najarzadeh
https://arxiv.org/abs/2508.17415

Asteroid Rotation Periods: Statistical Analysis in the Diameter-Spin Distribution
This study examines the rotational characteristics of asteroids through statistical modeling of the diameter-period relationship. A statistical evaluation of the diameter-period relationship was conducted using a dataset of 34,326 asteroids. Clustering identified three main groups, including a dense cluster below the spin barrier, a population of small, fast-rotating asteroids, and a more diffuse group. Geometric and density-based analyses showed that the densest region consists of objects with…

A clusterability test for directed graphs
Mario R. Guarracino, Pierre Miasnikof, Alexander Y. Shestopaloff, Houyem Demni, Cristi\'an Bravo, Yuri Lawryshyn
https://arxiv.org/abs/2506.20111

A clusterability test for directed graphs
In this article, we extend a statistical test of graph clusterability, the $δ$ test, to directed graphs with no self loops. The $δ$ test, originally designed for undirected graphs, is based on the premise that graphs with a clustered structure display a mean local density that is statistically higher than the graph's global density. We posit that graphs that do not meet this necessary (but not sufficient) condition for clusterability can be considered unsuited to clustering. In such cases, ve…

Optimizing Compilation for Distributed Quantum Computing via Clustering and Annealing
Ruilin Zhou, Jinglei Cheng, Yuhang Gan, Junyu Liu, Chen Qian
https://arxiv.org/abs/2508.15267

Optimizing Compilation for Distributed Quantum Computing via Clustering and Annealing
Efficiently mapping quantum programs onto Distributed quantum computing (DQC) are challenging, particularly when considering the heterogeneous quantum processing units (QPUs) with different structures. In this paper, we present a comprehensive compilation framework that addresses these challenges with three key insights: exploiting structural patterns within quantum circuits, using clustering for initial qubit placement, and adjusting qubit mapping with annealing algorithms. Experimental result…

An Enhanced Model-based Approach for Short Text Clustering
Enhao Cheng, Shoujia Zhang, Jianhua Yin, Xuemeng Song, Tian Gan, Liqiang Nie
https://arxiv.org/abs/2507.13793

An Enhanced Model-based Approach for Short Text Clustering
Short text clustering has become increasingly important with the popularity of social media like Twitter, Google+, and Facebook. Existing methods can be broadly categorized into two paradigms: topic model-based approaches and deep representation learning-based approaches. This task is inherently challenging due to the sparse, large-scale, and high-dimensional characteristics of the short text data. Furthermore, the computational intensity required by representation learning significantly increa…

Predicting wide binaries and deviations from standard gravity using machine learning algorithms
Amoy Ashesh, Harsimran Kaur, Sandeep Aashish
https://arxiv.org/abs/2506.19942

Predicting wide binaries and deviations from standard gravity using machine learning algorithms
We present a machine learning (ML) framework for the detection of wide binary star systems and the identification of deviations from standard gravity using Gaia DR3 data. By training supervised ML models on established wide binary catalogues, we efficiently classify wide binaries and employ clustering and nearest neighbour search to pair candidate systems. We recast the problem of identifying systematic deviations from standard gravity as a supervised anomaly detection. Our approach incorporate…

RoofSeg: An edge-aware transformer-based network for end-to-end roof plane segmentation
Siyuan You, Guozheng Xu, Pengwei Zhou, Qiwen Jin, Jian Yao, Li Li
https://arxiv.org/abs/2508.19003

RoofSeg: An edge-aware transformer-based network for end-to-end roof plane segmentation
Roof plane segmentation is one of the key procedures for reconstructing three-dimensional (3D) building models at levels of detail (LoD) 2 and 3 from airborne light detection and ranging (LiDAR) point clouds. The majority of current approaches for roof plane segmentation rely on the manually designed or learned features followed by some specifically designed geometric clustering strategies. Because the learned features are more powerful than the manually designed features, the deep learning-bas…

Learning graphons from data: Random walks, transfer operators, and spectral clustering
Stefan Klus, Jason J. Bramburger
https://arxiv.org/abs/2507.18147 https://

Learning graphons from data: Random walks, transfer operators, and spectral clustering
Many signals evolve in time as a stochastic process, randomly switching between states over discretely sampled time points. Here we make an explicit link between the underlying stochastic process of a signal that can take on a bounded continuum of values and a random walk process on a graphon. Graphons are infinite-dimensional objects that represent the limit of convergent sequences of graphs whose size tends to infinity. We introduce transfer operators, such as the Koopman and Perron--Frobeniu…

Clustering-based hard negative sampling for supervised contrastive speaker verification
Piotr Masztalski, Micha{\l} Romaniuk, Jakub \.Zak, Mateusz Matuszewski, Konrad Kowalczyk
https://arxiv.org/abs/2507.17540

Clustering-based hard negative sampling for supervised contrastive speaker verification
In speaker verification, contrastive learning is gaining popularity as an alternative to the traditionally used classification-based approaches. Contrastive methods can benefit from an effective use of hard negative pairs, which are different-class samples particularly challenging for a verification model due to their similarity. In this paper, we propose CHNS - a clustering-based hard negative sampling method, dedicated for supervised contrastive speaker representation learning. Our approach c…

FDTRImageEnhancer: Combining Physics-Informed Deconvolution and Microstructure-Aware Deep Learning to Enhance Thermal Images
Alesanmi Richmond Rerelope Odufisan
https://arxiv.org/abs/2508.16590

FDTRImageEnhancer: Combining Physics-Informed Deconvolution and Microstructure-Aware Deep Learning to Enhance Thermal Images
We present FDTRImageEnhancer, an open-source computational framework that improves thermal conductivity mapping from Frequency Domain ThermoReflectance (FDTR) phase data by integrating a physics-based Gaussian convolution abstraction with microstructure-aware deep learning. The Gaussian kernel models the spatial averaging effects of pump and probe beams, while k-means clustering of high-resolution structural images reduces the parameter space for inverse modeling. A physics-informed neural netw…

Gaussian filters in quantum lattice systems: Applications to spectral flow, local perturbations, clustering, and the quantum Hall effect
Sven Bachmann (Simon), Zhiqian (Simon), Du, Martin Fraas, Tom Wessel
https://arxiv.org/abs/2508.15913

Gaussian filters in quantum lattice systems: Applications to spectral flow, local perturbations, clustering, and the quantum Hall effect
We consider the locality and spectral properties of the smearing \[ τ_f(A) = \int_{-\infty}^\infty dt \, f(t) \, τ_t(A) \] when applied to the dynamics $τ_t$ of quantum spin systems. While recent applications of this map have used superpolynomially but not exponentially decaying functions $f$ to ensure exact spectral properties, we use here Gaussian filters. This improves the locality at the expense of errors on the spectral side. We propose a number of concrete applications, from quasi-adia…

Quantifying alpha clustering in the ground states of 16-O and 20-Ne
E. Harris, M. Barbui, J. Bishop, G. Chubarian, Sebastian Konig, E. Koshchiy, K. D. Launey, Dean Lee, Zifeng Luo, Yuan-Zhuo Ma, Ulf-G. Meissner, C. E. Parker, Zhengxue Ren, M. Roosa, A. Saastamoinen, G. H. Sargsyan, D. P. Scriven, Shihang Shen, A. Volya, Hang Yu, G. V. Rogachev
https://

Quantifying alpha clustering in the ground states of 16-O and 20-Ne
Understanding the role of multi-nucleon correlations in the structure of light nuclei is at the forefront of modern nuclear science. In this letter, we present a quantitative benchmark study of alpha-cluster correlations in the ground states of 16-O and 20-Ne. Experimental data provide direct evidence that the wave functions of the ground states of 16-O and 20-Ne are dominated by alpha-cluster correlations, in agreement with the predictions of sophisticated nuclear structure models. We also pro…

1.64-Approximation for Chromatic Correlation Clustering via Chromatic Cluster LP
Dahoon Lee, Chenglin Fan, Euiwoong Lee
https://arxiv.org/abs/2507.15417 ht…

1.64-Approximation for Chromatic Correlation Clustering via Chromatic Cluster LP
Chromatic Correlation Clustering (CCC) generalizes Correlation Clustering by assigning multiple categorical relationships (colors) to edges and imposing chromatic constraints on the clusters. Unlike traditional Correlation Clustering, which only deals with binary $(+/-)$ relationships, CCC captures richer relational structures. Despite its importance, improving the approximation for CCC has been difficult due to the limitations of standard LP relaxations. We present a randomized $1.64$-approxim…

Impact of pion tensor force on alpha clustering in $^{20}$Ne
Zhao Jing Chen, Bao Yuan Sun
https://arxiv.org/abs/2506.16947 https://ar…

Impact of pion tensor force on alpha clustering in $^{20}$Ne
The nuclear clustering, as a quantum phase transition phenomenon governed by strong interactions, exhibits characteristics that are highly sensitive to the specific features of nuclear forces. Here, we examine how nuclear deformation and tensor forces influence $α$-cluster formation in light nuclei. The axially deformed relativistic Hartree-Fock-Bogoliubov model is utilized to investigate the clustering structure of the $^{20}$Ne nucleus, at both the ground state and the excited state with a s…

Federated Learning based on Self-Evolving Gaussian Clustering
Miha O\v{z}bot, Igor \v{S}krjanc
https://arxiv.org/abs/2508.15393 https://arxiv.org/pdf/2508.…

Federated Learning based on Self-Evolving Gaussian Clustering
In this study, we present an Evolving Fuzzy System within the context of Federated Learning, which adapts dynamically with the addition of new clusters and therefore does not require the number of clusters to be selected apriori. Unlike traditional methods, Federated Learning allows models to be trained locally on clients' devices, sharing only the model parameters with a central server instead of the data. Our method, implemented using PyTorch, was tested on clustering and classification tasks…

Smart Charging Impact Analysis using Clustering Methods and Real-world Distribution Feeders
Ravi Raj Shrestha, Zhi Zhou, Limon Barua, Nazib Siddique, Karthikeyan Balasubramaniam, Yan Zhou, Lusha Wang
https://arxiv.org/abs/2508.15092

Smart Charging Impact Analysis using Clustering Methods and Real-world Distribution Feeders
The anticipated widespread adoption of electric vehicles (EVs) necessitates a critical evaluation of existing power distribution infrastructures, as EV integration imposes additional stress on distribution networks that can lead to component overloading and power quality degradation. Implementing smart charging mechanisms can mitigate these adverse effects and defer or even avoid upgrades. This study assesses the performance of two smart charging strategies - Time of Use (TOU) pricing and Load …

At #bbuzz 2025, Dennis Berger, Marco Petris, and Volker Carlguth discussed 'Intent-Based Clustering' to address limitations in hybrid search systems. They highlighted how LLM-supported query understanding enhances retrieval, clustering, validation, and presentation. Discover the journey from prototype to large-scale e-commerce search.
Watch the full session:

In the ISE2025 lecture today, our students learned about unsupervised learning on the example of k-Means clustering. One nice hands-on example is image colour reduction based on k-means clustering, as demonstrated in a colab notebook (based on the Python DataScience Handbook by Vanderplus)
colab notebook: https://…

The Shape of Consumer Behavior: A Symbolic and Topological Analysis of Time Series
Pola Bereta, Ioannis Diamantis
https://arxiv.org/abs/2506.19759 https://…

The Shape of Consumer Behavior: A Symbolic and Topological Analysis of Time Series
Understanding temporal patterns in online search behavior is crucial for real-time marketing and trend forecasting. Google Trends offers a rich proxy for public interest, yet the high dimensionality and noise of its time-series data present challenges for effective clustering. This study evaluates three unsupervised clustering approaches, Symbolic Aggregate approXimation (SAX), enhanced SAX (eSAX), and Topological Data Analysis (TDA), applied to 20 Google Trends keywords representing major cons…

Crosslisted article(s) found for math.PR. https://arxiv.org/list/math.PR/new
[1/1]:
- Clustering of quantum correlations at low temperature
Arka Adhikari, Joscha Henheik, Marius Lemm, Tom Wessel

A Hybrid Mixture Approach for Clustering and Characterizing Cancer Data
Kazeem Kareem, Fan Dai
https://arxiv.org/abs/2507.14380 https://

A Hybrid Mixture Approach for Clustering and Characterizing Cancer Data
Model-based clustering is widely used for identifying and distinguishing types of diseases. However, modern biomedical data coming with high dimensions make it challenging to perform the model estimation in traditional cluster analysis. The incorporation of factor analyzer into the mixture model provides a way to characterize the large set of data features, but the current estimation method is computationally impractical for massive data due to the intrinsic slow convergence of the embedded alg…

Machine learning based prediction of dynamical clustering in granular gases
Sai Preetham Sata, Ralf Stannarius, Benjamin Noack, Dmitry Puzyrev
https://arxiv.org/abs/2506.15657

Machine learning based prediction of dynamical clustering in granular gases
When dense granular gases are continuously excited under microgravity conditions, spatial inhomogeneities of the particle number density can emerge. A significant share of particles may collect in strongly overpopulated regions, called clusters. This dynamical clustering, or gas-cluster transition, is caused by a complex interplay and balance between the energy influx and dissipation in particle collisions. Particle number density, container geometry, and excitation strength influence this tran…

Analysing Models for Volatility Clustering with Subordinated Processes: VGSA and Beyond
Sourojyoti Barick, Sudip Ratan Chandra
https://arxiv.org/abs/2507.17431 https://

Analysing Models for Volatility Clustering with Subordinated Processes: VGSA and Beyond
This paper explores a comprehensive class of time-changed stochastic processes constructed by subordinating Brownian motion with Levy processes, where the subordination is further governed by stochastic arrival mechanisms such as the Cox Ingersoll Ross (CIR) and Chan Karolyi Longstaff Sanders (CKLS) processes. These models extend classical jump frameworks like the Variance Gamma (VG) and CGMY processes, allowing for more flexible modeling of market features such as jump clustering, heavy tails,…

Replaced article(s) found for hep-ph. https://arxiv.org/list/hep-ph/new
[2/2]:
- Little Red Dots from Small-Scale Primordial Black Hole Clustering
Borui Zhang, Wei-Xiang Feng, Haipeng An

Dark Degeneracy in DESI DR2: Interacting or Evolving Dark Energy?
Vitor Petri, Valerio Marra, Rodrigo von Marttens
https://arxiv.org/abs/2508.17955 https://

Dark Degeneracy in DESI DR2: Interacting or Evolving Dark Energy?
The standard $Λ$CDM model, despite its success, is challenged by persistent observational tensions in the Hubble constant ($H_0$) and the matter clustering amplitude ($S_8$), motivating the exploration of alternative cosmological scenarios. We investigate a dark energy model with a phenomenological interaction in the dark sector, constructed to be exactly degenerate at the background level with the Chevallier-Polarski-Linder (CPL) parameterization. This setup allows us to test whether models w…

Unsupervised Document and Template Clustering using Multimodal Embeddings
Phillipe R. Sampaio, Helene Maxcici
https://arxiv.org/abs/2506.12116 https://

Unsupervised Document and Template Clustering using Multimodal Embeddings
This paper investigates a novel approach to unsupervised document clustering by leveraging multimodal embeddings as input to traditional clustering algorithms such as $k$-Means and DBSCAN. Our method aims to achieve a finer-grained document understanding by not only grouping documents at the type level (e.g., invoices, purchase orders), but also distinguishing between different templates within the same document category. This is achieved by using embeddings that capture textual content, layout…

Dissecting the Segmentation Model of End-to-End Diarization with Vector Clustering
Alexis Plaquet, Naohiro Tawara, Marc Delcroix, Shota Horiguchi, Atsushi Ando, Shoko Araki, Herv\'e Bredin
https://arxiv.org/abs/2506.11605

Dissecting the Segmentation Model of End-to-End Diarization with Vector Clustering
End-to-End Neural Diarization with Vector Clustering is a powerful and practical approach to perform Speaker Diarization. Multiple enhancements have been proposed for the segmentation model of these pipelines, but their synergy had not been thoroughly evaluated. In this work, we provide an in-depth analysis on the impact of major architecture choices on the performance of the pipeline. We investigate different encoders (SincNet, pretrained and finetuned WavLM), different decoders (LSTM, Mamba, …

Neuromorphic Online Clustering and Its Application to Spike Sorting
James E. Smith
https://arxiv.org/abs/2506.12555 https://arxiv.org…

Neuromorphic Online Clustering and Its Application to Spike Sorting
Active dendrites are the basis for biologically plausible neural networks possessing many desirable features of the biological brain including flexibility, dynamic adaptability, and energy efficiency. A formulation for active dendrites using the notational language of conventional machine learning is put forward as an alternative to a spiking neuron formulation. Based on this formulation, neuromorphic dendrites are developed as basic neural building blocks capable of dynamic online clustering. …

Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning
Shashanka Venkataramanan, Valentinos Pariza, Mohammadreza Salehi, Lukas Knobel, Spyros Gidaris, Elias Ramzi, Andrei Bursuc, Yuki M. Asano
https://arxiv.org/abs/2507.14137

Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning
We present Franca (pronounced Fran-ka): free one; the first fully open-source (data, code, weights) vision foundation model that matches and in many cases surpasses the performance of state-of-the-art proprietary models, e.g., DINOv2, CLIP, SigLIPv2, etc. Our approach is grounded in a transparent training pipeline inspired by Web-SSL and uses publicly available data: ImageNet-21K and a subset of ReLAION-2B. Beyond model release, we tackle critical limitations in SSL clustering methods. While mo…

Exact Matrix Seriation through Mathematical Optimization: Stress and Effectiveness-Based Models
V\'ictor Blanco, Alfredo Mar\'in, Justo Puerto
https://arxiv.org/abs/2506.19821

Exact Matrix Seriation through Mathematical Optimization: Stress and Effectiveness-Based Models
Matrix seriation, the problem of permuting the rows and columns of a matrix to uncover latent structure, is a fundamental technique in data science, particularly in the visualization and analysis of relational data. Applications span clustering, anomaly detection, and beyond. In this work, we present a unified framework grounded in mathematical optimization to address matrix seriation from a rigorous, model-based perspective. Our approach leverages combinatorial and mixed-integer optimization t…

Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiatio
Yaowen Hu, Wenxuan Tu, Yue Liu, Xinhang Wan, Junyi Yan, Taichun Zhou, Xinwang Liu
https://arxiv.org/abs/2507.13368

Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiatio
Deep graph clustering (DGC), which aims to unsupervisedly separate the nodes in an attribute graph into different clusters, has seen substantial potential in various industrial scenarios like community detection and recommendation. However, the real-world attribute graphs, e.g., social networks interactions, are usually large-scale and attribute-missing. To solve these two problems, we propose a novel DGC method termed \underline{\textbf{C}}omplementary \underline{\textbf{M}}ulti-\underline{\te…

Replaced article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/1]:
- Stable and consistent density-based clustering via multiparameter persistence
Alexander Rolle, Luis Scoccola

Understanding uncertainty in Bayesian cluster analysis
Cecilia Balocchi, Sara Wade
https://arxiv.org/abs/2506.16295 https://arxiv.org…

Understanding uncertainty in Bayesian cluster analysis
The Bayesian approach to clustering is often appreciated for its ability to provide uncertainty in the partition structure. However, summarizing the posterior distribution over the clustering structure can be challenging, due the discrete, unordered nature and massive dimension of the space. While recent advancements provide a single clustering estimate to represent the posterior, this ignores uncertainty and may even be unrepresentative in instances where the posterior is multimodal. To enhanc…

Disformal interactions in the Dark Sector: From driving Early Dark Energy to confronting cosmological tensions
Pulkit Bansal (IIT Bombay), Joseph P. Johnson (IISER Mohali), S. Shankaranarayanan (IIT Bombay)
https://arxiv.org/abs/2508.17003

Disformal interactions in the Dark Sector: From driving Early Dark Energy to confronting cosmological tensions
The $Λ$CDM model faces significant challenges, including an incomplete understanding of the dark sector and persistent tensions in the Hubble constant and the clustering amplitude. To address these issues, we propose a general disformal coupling between dark energy (DE) and dark matter from a field-theoretic action which can generate a rich variety of interactions including conformal and pure-momentum coupling scenarios. Our analysis reveals that a pure disformal coupling naturally produces a …

Fronthaul-Aware User-Centric Generalized Cell-Free Massive MIMO Systems
Zahra Mobini, Ahmet Hasim Gokceoglu, Li Wang, Gunnar Peters, Hien Quoc Ngo
https://arxiv.org/abs/2506.14494

Fronthaul-Aware User-Centric Generalized Cell-Free Massive MIMO Systems
We consider fronthaul-limited generalized zeroforcing-based cell-free massive multiple-input multiple-output (CF-mMIMO) systems with multiple-antenna users and multipleantenna access points (APs) relying on both cooperative beamforming (CB) and user-centric (UC) clustering. The proposed framework is very general and can be degenerated into different special cases, such as pure CB/pure UC clustering, or fully centralized CB/fully distributed beamforming. We comprehensively analyze the spectral e…

Spatio-spectral diarization of meetings by combining TDOA-based segmentation and speaker embedding-based clustering
Tobias Cord-Landwehr, Tobias Gburrek, Marc Deegen, Reinhold Haeb-Umbach
https://arxiv.org/abs/2506.16228

Spatio-spectral diarization of meetings by combining TDOA-based segmentation and speaker embedding-based clustering
We propose a spatio-spectral, combined model-based and data-driven diarization pipeline consisting of TDOA-based segmentation followed by embedding-based clustering. The proposed system requires neither access to multi-channel training data nor prior knowledge about the number or placement of microphones. It works for both a compact microphone array and distributed microphones, with minor adjustments. Due to its superior handling of overlapping speech during segmentation, the proposed pipeline …

The Large Magellanic Cloud as a source of the highest energy cosmic rays
Roger Clay, Tadeusz Wibig
https://arxiv.org/abs/2507.16378 https://

The Large Magellanic Cloud as a source of the highest energy cosmic rays
The Pierre Auger Observatory has published properties of the 100 highest-energy cosmic ray events (to energies above 100 EeV) which it recorded over a 17 year period. We have examined the directional properties of these events and have taken particular note of the most energetically extreme events. We find that the most energetic events have directions which are grouped in a non-random way at the 1\% level. There is an apparent clustering in a limited region of the sky. Close to that direction …

Explainable Evidential Clustering
Victor F. Lopes de Souza, Karima Bakhti, Sofiane Ramdani, Denis Mottet, Abdelhak Imoussaten
https://arxiv.org/abs/2507.12192

Explainable Evidential Clustering
Unsupervised classification is a fundamental machine learning problem. Real-world data often contain imperfections, characterized by uncertainty and imprecision, which are not well handled by traditional methods. Evidential clustering, based on Dempster-Shafer theory, addresses these challenges. This paper explores the underexplored problem of explaining evidential clustering results, which is crucial for high-stakes domains such as healthcare. Our analysis shows that, in the general case, repr…

Multiple Hypothesis Testing To Estimate The Number Of Communities in Stochastic Block Models
Chetkar Jha, Mingyao Li, Ian Barnett
https://arxiv.org/abs/2507.15471

Multiple Hypothesis Testing To Estimate The Number Of Communities in Stochastic Block Models
Clustering of single-cell RNA sequencing (scRNA-seq) datasets can give key insights into the biological functions of cells. Therefore, it is not surprising that network-based community detection methods (one of the better clustering methods) are increasingly being used for the clustering of scRNA-seq datasets. The main challenge in implementing network-based community detection methods for scRNA-seq datasets is that these methods \emph{apriori} require the true number of communities or blocks f…

Interpretable Text-Guided Image Clustering via Iterative Search
Bingchen Zhao, Oisin Mac Aodha
https://arxiv.org/abs/2506.12514 https://

Interpretable Text-Guided Image Clustering via Iterative Search
Traditional clustering methods aim to group unlabeled data points based on their similarity to each other. However, clustering, in the absence of additional information, is an ill-posed problem as there may be many different, yet equally valid, ways to partition a dataset. Distinct users may want to use different criteria to form clusters in the same data, e.g. shape v.s. color. Recently introduced text-guided image clustering methods aim to address this ambiguity by allowing users to specify t…

K-Means Panel Data Clustering in the Presence of Small Groups
Mikihito Nishi
https://arxiv.org/abs/2508.15408 https://arxiv.org/pdf/2508.15408

K-Means Panel Data Clustering in the Presence of Small Groups
We consider panel data models with group structure. We study the asymptotic behavior of least-squares estimators and information criterion for the number of groups, allowing for the presence of small groups that have an asymptotically negligible relative size. Our contributions are threefold. First, we derive sufficient conditions under which the least-squares estimators are consistent and asymptotically normal. One of the conditions implies that a longer sample period is required as there are …

Minimum Clustering of Matrices Based on Phase Alignment
Honghao Wu, Kemi Ding, Li Qiu
https://arxiv.org/abs/2507.13678 https://arxiv.…

Minimum Clustering of Matrices Based on Phase Alignment
Coordinating multi-agent systems requires balancing synchronization performance and controller implementation costs. To this end, we classify agents by their intrinsic properties, enabling each group to be controlled by a uniform controller and thus reducing the number of unique controller types required. Existing centralized control methods, despite their capability to achieve high synchronization performance with fewer types of controllers, suffer from critical drawbacks such as limited scala…

Score Matching on Large Geometric Graphs for Cosmology Generation
Diana-Alexandra Onutu, Yue Zhao, Joaquin Vanschoren, Vlado Menkovski
https://arxiv.org/abs/2508.16990 https://

Score Matching on Large Geometric Graphs for Cosmology Generation
Generative models are a promising tool to produce cosmological simulations but face significant challenges in scalability, physical consistency, and adherence to domain symmetries, limiting their utility as alternatives to $N$-body simulations. To address these limitations, we introduce a score-based generative model with an equivariant graph neural network that simulates gravitational clustering of galaxies across cosmologies starting from an informed prior, respects periodic boundaries, and s…

Fair Bayesian Model-Based Clustering
Jihu Lee, Kunwoong Kim, Yongdai Kim
https://arxiv.org/abs/2506.12839 https://arxiv.org/pdf/2506.…

Fair Bayesian Model-Based Clustering
Fair clustering has become a socially significant task with the advancement of machine learning technologies and the growing demand for trustworthy AI. Group fairness ensures that the proportions of each sensitive group are similar in all clusters. Most existing group-fair clustering methods are based on the $K$-means clustering and thus require the distance between instances and the number of clusters to be given in advance. To resolve this limitation, we propose a fair Bayesian model-based cl…

Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
Donovan Slabbert, Dean Brand, Francesco Petruccione
https://arxiv.org/abs/2507.07018

Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
We compare a parameterized quantum kernel (pQK) with a quantum leaky integrate-and-fire (QLIF) neuromorphic computing approach that employs either the Victor-Purpura or van Rossum kernel in a spectral clustering task, as well as the classical radial basis function (RBF) kernel. Performance evaluation includes label-based classification and clustering metrics, as well as optimal number of clusters predictions for each dataset based on an elbow-like curve as is typically used in $K$-means cluster…

Game-Theoretic and Reinforcement Learning-Based Cluster Head Selection for Energy-Efficient Wireless Sensor Network
Mehrshad Eskandarpour, Saba Pirahmadian, Parham Soltani, Hossein Soleimani
https://arxiv.org/abs/2508.12707

Game-Theoretic and Reinforcement Learning-Based Cluster Head Selection for Energy-Efficient Wireless Sensor Network
Energy in Wireless Sensor Networks (WSNs) is critical to network lifetime and data delivery. However, the primary impediment to the durability and dependability of these sensor nodes is their short battery life. Currently, power-saving algorithms such as clustering and routing algorithms have improved energy efficiency in standard protocols. This paper proposes a clustering-based routing approach for creating an adaptive, energy-efficient mechanism. Our system employs a multi-step clustering st…

Measures of Overlapping Multivariate Gaussian Clusters in Unsupervised Online Learning
Miha O\v{z}bot, Igor \v{S}krjanc
https://arxiv.org/abs/2508.15444 https://

Measures of Overlapping Multivariate Gaussian Clusters in Unsupervised Online Learning
In this paper, we propose a new measure for detecting overlap in multivariate Gaussian clusters. The aim of online learning from data streams is to create clustering, classification, or regression models that can adapt over time based on the conceptual drift of streaming data. In the case of clustering, this can result in a large number of clusters that may overlap and should be merged. Commonly used distribution dissimilarity measures are not adequate for determining overlapping clusters in th…

Replaced article(s) found for stat.AP. https://arxiv.org/list/stat.AP/new
[1/1]:
- An Efficient Image Denoising Method Integrating Multi-resolution Local Clustering and Adaptive Sm...
Subhasish Basak, Partha Sarathi Mukherjee

End-to-End Diarization utilizing Attractor Deep Clustering
David Palzer, Matthew Maciejewski, Eric Fosler-Lussier
https://arxiv.org/abs/2506.11090 https://…

End-to-End Diarization utilizing Attractor Deep Clustering
Speaker diarization remains challenging due to the need for structured speaker representations, efficient modeling, and robustness to varying conditions. We propose a performant, compact diarization framework that integrates conformer decoders, transformer-updated attractors, and a deep clustering style angle loss. Our approach refines speaker representations with an enhanced conformer structure, incorporating cross-attention to attractors and an additional convolution module. To enforce struct…

Relative Error Fair Clustering in the Weak-Strong Oracle Model
Vladimir Braverman, Prathamesh Dharangutte, Shaofeng H. -C. Jiang, Hoai-An Nguyen, Chen Wang, Yubo Zhang, Samson Zhou
https://arxiv.org/abs/2506.12287

Relative Error Fair Clustering in the Weak-Strong Oracle Model
We study fair clustering problems in a setting where distance information is obtained from two sources: a strong oracle providing exact distances, but at a high cost, and a weak oracle providing potentially inaccurate distance estimates at a low cost. The goal is to produce a near-optimal fair clustering on $n$ input points with a minimum number of strong oracle queries. This models the increasingly common trade-off between accurate but expensive similarity measures (e.g., large-scale embedding…

Local Clustering in Hypergraphs through Higher-Order Motifs
Giuseppe F. Italiano, Athanasios L. Konstantinidis, Anna Mpanti, Fariba Ranjbar
https://arxiv.org/abs/2507.10570

Local Clustering in Hypergraphs through Higher-Order Motifs
Hypergraphs provide a powerful framework for modeling complex systems and networks with higher-order interactions beyond simple pairwise relationships. However, graph-based clustering approaches, which focus primarily on pairwise relations, fail to represent higher-order interactions, often resulting in low-quality clustering outcomes. In this work, we introduce a novel approach for local clustering in hypergraphs based on higher-order motifs, small connected subgraphs in which nodes may be lin…

Alpha clustering in warm and dense nuclear matter from heavy-ion collisions
Rui Wang, Zhen Zhang, Yu-Gang Ma, Lie-Wen Chen, Che Ming Ko, Kai-Jia Sun
https://arxiv.org/abs/2507.16613

Alpha clustering in warm and dense nuclear matter from heavy-ion collisions
Although light nuclear clusters are known to affect the properties of warm and dilute nuclear matter, their role in warm and dense nuclear matter remains unclear due to the lack of experimental evidence for their modifications by the Mott effect in such an environment. To address this issue, we resort to intermediate-energy heavy-ion collisions, where light clusters are mainly produced in the transiently formed warm and dense matter. A kinetic approach, which includes dynamically the formation …

A Bayesian Semiparametric Mixture Model for Clustering Zero-Inflated Microbiome Data
Suppapat Korsurat, Matthew D. Koslovsky
https://arxiv.org/abs/2508.14184 https://

A Bayesian Semiparametric Mixture Model for Clustering Zero-Inflated Microbiome Data
Microbiome research has immense potential for unlocking insights into human health and disease. A common goal in human microbiome research is identifying subgroups of individuals with similar microbial composition that may be linked to specific health states or environmental exposures. However, existing clustering methods are often not equipped to accommodate the complex structure of microbiome data and typically make limiting assumptions regarding the number of clusters in the data which can b…

Unsupervised Urban Tree Biodiversity Mapping from Street-Level Imagery Using Spatially-Aware Visual Clustering
Diaa Addeen Abuhani, Marco Seccaroni, Martina Mazzarello, Imran Zualkernan, Fabio Duarte, Carlo Ratti
https://arxiv.org/abs/2508.13814

Unsupervised Urban Tree Biodiversity Mapping from Street-Level Imagery Using Spatially-Aware Visual Clustering
Urban tree biodiversity is critical for climate resilience, ecological stability, and livability in cities, yet most municipalities lack detailed knowledge of their canopies. Field-based inventories provide reliable estimates of Shannon and Simpson diversity but are costly and time-consuming, while supervised AI methods require labeled data that often fail to generalize across regions. We introduce an unsupervised clustering framework that integrates visual embeddings from street-level imagery …

Extracting Overlapping Microservices from Monolithic Code via Deep Semantic Embeddings and Graph Neural Network-Based Soft Clustering
Morteza Ziabakhsh, Kiyan Rezaee, Sadegh Eskandari, Seyed Amir Hossein Tabatabaei, Mohammad M. Ghassemi
https://arxiv.org/abs/2508.07486

Extracting Overlapping Microservices from Monolithic Code via Deep Semantic Embeddings and Graph Neural Network-Based Soft Clustering
Modern software systems are increasingly shifting from monolithic architectures to microservices to enhance scalability, maintainability, and deployment flexibility. Existing microservice extraction methods typically rely on hard clustering, assigning each software component to a single microservice. This approach often increases inter-service coupling and reduces intra-service cohesion. We propose Mo2oM (Monolithic to Overlapping Microservices), a framework that formulates microservice extract…

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…

Robust Multi-Manifold Clustering via Simplex Paths
Haoyu Chen, Anna Little, Akin Narayan
https://arxiv.org/abs/2507.10710 https://arx…

Robust Multi-Manifold Clustering via Simplex Paths
This article introduces a novel, geometric approach for multi-manifold clustering (MMC), i.e. for clustering a collection of potentially intersecting, d-dimensional manifolds into the individual manifold components. We first compute a locality graph on d-simplices, using the dihedral angle in between adjacent simplices as the graph weights, and then compute infinity path distances in this simplex graph. This procedure gives a metric on simplices which we refer to as the largest angle path dista…

Learning clusters of partially observed linear dynamical systems
Maryann Rui, Munther A. Dahleh
https://arxiv.org/abs/2507.17638 https://arxiv.org/pdf/2507…

Learning clusters of partially observed linear dynamical systems
We study the problem of learning clusters of partially observed linear dynamical systems from multiple input-output trajectories. This setting is particularly relevant when there are limited observations (e.g., short trajectories) from individual data sources, making direct estimation challenging. In such cases, incorporating data from multiple related sources can improve learning. We propose an estimation algorithm that leverages different data requirements for the tasks of clustering and syst…

Clustering-based aggregate value regression
Kei Hirose, Hidetoshi Matsui, Hiroki Masuda
https://arxiv.org/abs/2508.15567 https://arxiv.org/pdf/2508.15567…

Clustering-based aggregate value regression
In various practical situations, forecasting of aggregate values rather than individual ones is often our main focus. For instance, electricity companies are interested in forecasting the total electricity demand in a specific region to ensure reliable grid operation and resource allocation. However, to our knowledge, statistical learning specifically for forecasting aggregate values has not yet been well-established. In particular, the relationship between forecast error and the number of clus…

Rethinking VAE: From Continuous to Discrete Representations Without Probabilistic Assumptions
Songxuan Shi
https://arxiv.org/abs/2507.17255 https://…

Rethinking VAE: From Continuous to Discrete Representations Without Probabilistic Assumptions
This paper explores the generative capabilities of Autoencoders (AEs) and establishes connections between Variational Autoencoders (VAEs) and Vector Quantized-Variational Autoencoders (VQ-VAEs) through a reformulated training framework. We demonstrate that AEs exhibit generative potential via latent space interpolation and perturbation, albeit limited by undefined regions in the encoding space. To address this, we propose a new VAE-like training method that introduces clustering centers to enha…

Competitively Consistent Clustering
Niv Buchbinder, Roie Levin, Yue Yang
https://arxiv.org/abs/2508.10800 https://arxiv.org/pdf/2508.10800

Competitively Consistent Clustering
In fully-dynamic consistent clustering, we are given a finite metric space $(M,d)$, and a set $F\subseteq M$ of possible locations for opening centers. Data points arrive and depart, and the goal is to maintain an approximately optimal clustering solution at all times while minimizing the recourse, the total number of additions/deletions of centers over time. Specifically, we study fully dynamic versions of the classical $k$-center, facility location, and $k$-median problems. We design algorith…

Frequentist Cosmological Constraints from Full-Shape Clustering Measurements in DESI DR1
James Morawetz, Hanyu Zhang, Marco Bonici, Will Percival, Andrea Crespi, Jessica Nicole Aguilar, Steven Ahlen, Davide Bianchi, David Brooks, Francisco Javier Castander, Todd Claybaugh, Shaun Cole, Andrei Cuceu, Axel de la Macorra, Arnaud de Mattia, Biprateep Dey, Peter Doel, Simone Ferraro, Andreu Font-Ribera, Jaime E. Forero-Romero, Enrique Gazta\~naga, Satya Gontcho A Gontcho, Gaston Gutierrez, C…

Frequentist Cosmological Constraints from Full-Shape Clustering Measurements in DESI DR1
We perform a frequentist analysis using the standard profile likelihood method for clustering measurements from Data Release 1 of the Dark Energy Spectroscopic Instrument (DESI). While Bayesian inferences for Effective Field Theory models of galaxy clustering can be highly sensitive to the choice of priors for extended cosmological models, frequentist inferences are not susceptible to such effects. We compare Bayesian and frequentist constraints for the parameter set $\{σ_8, H_0, Ω_{\rm{m}}, …

GOLFS: Feature Selection via Combining Both Global and Local Information for High Dimensional Clustering
Zhaoyu Xing, Yang Wan, Juan Wen, Wei Zhong
https://arxiv.org/abs/2507.10956

GOLFS: Feature Selection via Combining Both Global and Local Information for High Dimensional Clustering
It is important to identify the discriminative features for high dimensional clustering. However, due to the lack of cluster labels, the regularization methods developed for supervised feature selection can not be directly applied. To learn the pseudo labels and select the discriminative features simultaneously, we propose a new unsupervised feature selection method, named GlObal and Local information combined Feature Selection (GOLFS), for high dimensional clustering problems. The GOLFS algori…

Policy-Based Trajectory Clustering in Offline Reinforcement Learning
Hao Hu, Xinqi Wang, Simon Shaolei Du
https://arxiv.org/abs/2506.09202 https://

Policy-Based Trajectory Clustering in Offline Reinforcement Learning
We introduce a novel task of clustering trajectories from offline reinforcement learning (RL) datasets, where each cluster center represents the policy that generated its trajectories. By leveraging the connection between the KL-divergence of offline trajectory distributions and a mixture of policy-induced distributions, we formulate a natural clustering objective. To solve this, we propose Policy-Guided K-means (PG-Kmeans) and Centroid-Attracted Autoencoder (CAAE). PG-Kmeans iteratively trains…

Regularized k-POD: Sparse k-means clustering for high-dimensional missing data
Xin Guan, Yoshikazu Terada
https://arxiv.org/abs/2507.11884 https://

Regularized k-POD: Sparse k-means clustering for high-dimensional missing data
The classical k-means clustering, based on distances computed from all data features, cannot be directly applied to incomplete data with missing values. A natural extension of k-means to missing data, namely k-POD, uses only the observed entries for clustering and is both computationally efficient and flexible. However, for high-dimensional missing data including features irrelevant to the underlying cluster structure, the presence of such irrelevant features leads to the bias of k-POD in estim…

Testing Signatures of Phantom Crossing through Full-Shape Galaxy Clustering Analysis
Emanuelly Silva, Rafael C. Nunes
https://arxiv.org/abs/2507.13989 http…

Testing Signatures of Phantom Crossing through Full-Shape Galaxy Clustering Analysis
Recent observations of baryon acoustic oscillations (BAO) from the Dark Energy Spectroscopic Instrument (DESI) survey, when combined with measurements of the cosmic microwave background (CMB) and Type Ia supernovae (SNIa), provide compelling evidence for a phantom crossing at late times, along with statistically significant deviations from the standard $Λ$CDM model. In this work, we investigate the role of redshift-space galaxy clustering data by employing the pre-reconstruction full-shape (FS…

Detecting Patterns of Interaction in Temporal Hypergraphs via Edge Clustering
Ryan DeWolfe, Fran\c{c}ois Th\'eberge
https://arxiv.org/abs/2506.03105 ht…

Detecting Patterns of Interaction in Temporal Hypergraphs via Edge Clustering
Finding densely connected subsets of vertices in an unsupervised setting, called clustering or community detection, is one of the fundamental problems in network science. The edge clustering approach instead detects communities by clustering the edges of the graph and then assigning a vertex to a community if it has at least one edge in that community, thereby allowing for overlapping clusters of vertices. We apply the idea behind edge clustering to temporal hypergraphs, an extension of a graph…

SPHENIC: Topology-Informed Multi-View Clustering for Spatial Transcriptomics
Chenkai Guo, Yikai Zhu, Jing Yangum, Renxiang Guan, Por Lip Yee, Guangdun Peng, Dayu Hu
https://arxiv.org/abs/2508.10646

SPHENIC: Topology-Informed Multi-View Clustering for Spatial Transcriptomics
By incorporating spatial location information, spatial-transcriptomics clustering yields more comprehensive insights into cell subpopulation identification. Despite recent progress, existing methods have at least two limitations: (i) topological learning typically considers only representations of individual cells or their interaction graphs; however, spatial transcriptomic profiles are often noisy, making these approaches vulnerable to low-quality topological signals, and (ii) insufficient mod…

Derivative-Agnostic Inference of Nonlinear Hybrid Systems
Hengzhi Yu, Bohan Ma, Mingshuai Chen, Jie An, Bin Gu, Naijun Zhan, Jianwei Yin
https://arxiv.org/abs/2507.16426

Derivative-Agnostic Inference of Nonlinear Hybrid Systems
This paper addresses the problem of inferring a hybrid automaton from a set of input-output traces of a hybrid system exhibiting discrete mode switching between continuously evolving dynamics. Existing approaches mainly adopt a derivative-based method where (i) the occurrence of mode switching is determined by a drastic variation in derivatives and (ii) the clustering of trace segments relies on signal similarity -- both subject to user-supplied thresholds. We present a derivative-agnostic appr…

Inference of matter power spectrum at z=0 using DESI DR1 Full-Shape data
R. Cereskaite, E. Mueller, C. Howlett, Tamara M. Davis, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, F. J. Castander, T. Claybaugh, A. Cuceu, A. de la Macorra, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, E. Gaztanaga, G. Gutierrez, C. Hahn, K. Honscheid, D. Huterer, M. Ishak, R. Joyce, S. Juneau, D. Kirkby, A. Kremin, O. Lahav, A. Lambert, M. Landriau, L. Le Guillou, M. Manera, A. Meisner, R. Miquel, J. Moust…

Inference of matter power spectrum at z=0 using DESI DR1 Full-Shape data
Measurements of galaxy distributions at large cosmic distances capture clustering from the past. In this study, we use a cosmological model to translate these observations into the present-day galaxy distribution. Specifically, we reconstruct the 3D matter power spectrum at redshift $ z = 0 $ using Dark Energy Spectroscopic Instrument (DESI) Year 1 (DR1) galaxy clustering data and Cosmic Microwave Background (CMB) observations, assuming the $ Λ\text{CDM} $ model, and compare it to the result a…

Near-perfect Clustering Based on Recursive Binary Splitting Using Max-MMD
Sourav Chakrabarty, Anirvan Chakraborty, Shyamal K. De
https://arxiv.org/abs/2507.11158

Near-perfect Clustering Based on Recursive Binary Splitting Using Max-MMD
We develop novel clustering algorithms for functional data when the number of clusters $K$ is unknown and also when it is prefixed. These algorithms are developed based on the Maximum Mean Discrepancy (MMD) measure between two sets of observations. The algorithms recursively use a binary splitting strategy to partition the dataset into two subgroups such that they are maximally separated in terms of an appropriate weighted MMD measure. When $K$ is unknown, the proposed clustering algorithm has …

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…

Optimising the sample selection for photometric galaxy surveys
Marc Alemany-Gotor, Isaac Tutusaus, Pablo Fosalba
https://arxiv.org/abs/2507.18142 https://a…

Optimising the sample selection for photometric galaxy surveys
Determining cosmological parameters with high precision, as well as resolving current tensions in their values derived from low and high redshift probes, is one of the main objectives of the new generation of cosmological surveys. The combination of complementary probes in terms of parameter degeneracies and systematics is key to achieving these ambitious scientific goals. In this context, determining the optimal survey configuration for an analysis that combines galaxy clustering, weak lensing…

CountCluster: Training-Free Object Quantity Guidance with Cross-Attention Map Clustering for Text-to-Image Generation
Joohyeon Lee, Jin-Seop Lee, Jee-Hyong Lee
https://arxiv.org/abs/2508.10710

CountCluster: Training-Free Object Quantity Guidance with Cross-Attention Map Clustering for Text-to-Image Generation
Diffusion-based text-to-image generation models have demonstrated strong performance in terms of image quality and diversity. However, they still struggle to generate images that accurately reflect the number of objects specified in the input prompt. Several approaches have been proposed that rely on either external counting modules for iterative refinement or quantity representations derived from learned tokens or latent features. However, they still have limitations in accurately reflecting t…

Efficient Integration of Multi-View Attributed Graphs for Clustering and Embedding
Yiran Li, Gongyao Guo, Jieming Shi, Sibo Wang, Qing Li
https://arxiv.org/abs/2508.09452 https:…

Efficient Integration of Multi-View Attributed Graphs for Clustering and Embedding
A multi-view attributed graph (MVAG) G captures the diverse relationships and properties of real-world entities through multiple graph views and attribute views. Effectively utilizing all views in G is essential for MVAG clustering and embedding, which are important for applications like recommendation systems, anomaly detection, social network analysis, etc. Existing methods either achieve inferior result quality or incur significant computational costs to handle large-scale MVAGs. In this p…

Integer-Clustering Optimization of Hydrogen and Battery EV Fleets Considering DERs
Sijia Geng, Thomas Lee, Dharik Mallapragada, Audun Botterud
https://arxiv.org/abs/2506.09388

Integer-Clustering Optimization of Hydrogen and Battery EV Fleets Considering DERs
Electrified transportation leads to a tighter integration between transportation and energy distribution systems. In this work, we develop scalable optimization models to co-design hydrogen and battery electric vehicle (EV) fleets, distributed energy resources, and fast-charging and hydrogen-fueling infrastructure to efficiently meet transportation demands. A novel integer-clustering formulation is used for optimizing fleet-level EV operation while maintaining accurate individual vehicle dispat…

A Robust Nonparametric Framework for Detecting Repeated Spatial Patterns
Rajitha Senanayake, Pratheepa Jeganathan
https://arxiv.org/abs/2506.14103 https://…

A Robust Nonparametric Framework for Detecting Repeated Spatial Patterns
Identifying spatially contiguous clusters and repeated spatial patterns (RSP) characterized by similar underlying distributions that are spatially apart is a key challenge in modern spatial statistics. Existing constrained clustering methods enforce spatial contiguity but are limited in their ability to identify RSP. We propose a novel nonparametric framework that addresses this limitation by combining constrained clustering with a post-clustering reassigment step based on the maximum mean disc…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/8]:
- Beyond Win Rates: A Clustering-Based Approach to Character Balance Analysis in Team-Based Games
Haokun Zhou

Addressing overlapping communities in multiple-source detection: An edge clustering approach for complex networks
Haomin Li, Daniel K. Sewell
https://arxiv.org/abs/2507.08265

Addressing overlapping communities in multiple-source detection: An edge clustering approach for complex networks
The source detection problem in network analysis involves identifying the origins of diffusion processes, such as disease outbreaks or misinformation propagation. Traditional methods often focus on single sources, whereas real-world scenarios frequently involve multiple sources, complicating detection efforts. This study addresses the multiple-source detection (MSD) problem by integrating edge clustering algorithms into the community-based label propagation framework, effectively handling mixed…

On Tight Robust Coresets for $k$-Medians Clustering
Lingxiao Huang, Zhenyu Jiang, Yi Li, Xuan Wu
https://arxiv.org/abs/2507.11260 https://

On Tight Robust Coresets for $k$-Medians Clustering
This paper considers coresets for the robust $k$-medians problem with $m$ outliers, and new constructions in various metric spaces are obtained. Specifically, for metric spaces with a bounded VC or doubling dimension $d$, the coreset size is $O(m) + \tilde{O}(kd\varepsilon^{-2})$, which is optimal up to logarithmic factors. For Euclidean spaces, the coreset size is $O(m\varepsilon^{-1}) + \tilde{O}(\min\{k^{4/3}\varepsilon^{-2},k\varepsilon^{-3}\})$, improving upon a recent result by Jiang and …

funOCLUST: Clustering Functional Data with Outliers
Katharine M. Clark, Paul D. McNicholas
https://arxiv.org/abs/2508.00110 https://arxiv.org/pdf/2508.0011…

funOCLUST: Clustering Functional Data with Outliers
Functional data present unique challenges for clustering due to their infinite-dimensional nature and potential sensitivity to outliers. An extension of the OCLUST algorithm to the functional setting is proposed to address these issues. The approach leverages the OCLUST framework, creating a robust method to cluster curves and trim outliers. The methodology is evaluated on both simulated and real-world functional datasets, demonstrating strong performance in clustering and outlier identificatio…

Consistent Bayesian Spatial Domain Partitioning Using Predictive Spanning Tree Methods
Kun Huang, Huiyan Sang
https://arxiv.org/abs/2508.08324 https://arxi…

Consistent Bayesian Spatial Domain Partitioning Using Predictive Spanning Tree Methods
Bayesian model-based spatial clustering methods are widely used for their flexibility in estimating latent clusters with an unknown number of clusters while accounting for spatial proximity. Many existing methods are designed for clustering finite spatial units, limiting their ability to make predictions, or may impose restrictive geometric constraints on the shapes of subregions. Furthermore, the posterior clustering consistency theory of spatial clustering models remains largely unexplored in…

FuGa3D: Fast full-sky analysis of Galaxy catalogs in 3D
Elina Keih\"anen, Jani Haapala, Valtteri Lindholm, Martin Reinecke, Susan Rissanen, Jussi Valiviita, Akke Viitanen
https://arxiv.org/abs/2506.18676

FuGa3D: Fast full-sky analysis of Galaxy catalogs in 3D
We present FuGa3D, a code for fast computation of correlation functions and power spectra for galaxy survey observables, including galaxy clustering and cosmic shear. We define the redshift-space correlation function (RCF) as the correlation function defined in the parameter space of two redshifts and an angular separation angle. Assuming that there is no preferred direction in the sky, these parameters fully define the relative position of two galaxies, independently of the assumed cosmologica…

This https://arxiv.org/abs/2505.22984 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

A Computational Approach to Improving Fairness in K-means Clustering
The popular K-means clustering algorithm potentially suffers from a major weakness for further analysis or interpretation. Some cluster may have disproportionately more (or fewer) points from one of the subpopulations in terms of some sensitive variable, e.g., gender or race. Such a fairness issue may cause bias and unexpected social consequences. This work attempts to improve the fairness of K-means clustering with a two-stage optimization formulation--clustering first and then adjust cluster …

Context Steering: A New Paradigm for Compression-based Embeddings by Synthesizing Relevant Information Features
Guillermo Sarasa Dur\'an, Ana Granados Fontecha, Francisco de Borja Rodr\'iguez Ort\'iz
https://arxiv.org/abs/2508.14780

Context Steering: A New Paradigm for Compression-based Embeddings by Synthesizing Relevant Information Features
Compression-based distances (CD) offer a flexible and domain-agnostic means of measuring similarity by identifying implicit information through redundancies between data objects. However, as similarity features are derived from the data, rather than defined as an input, it often proves difficult to align with the task at hand, particularly in complex clustering or classification settings. To address this issue, we introduce "context steering," a novel methodology that actively guides the featur…

Robust fuzzy clustering with cellwise outliers
Giorgia Zaccaria, Lorenzo Benzakour, Luis A. Garc\'ia-Escudero, Francesca Greselin, Agust\'in Mayo-\'Iscar
https://arxiv.org/abs/2508.03310

Robust fuzzy clustering with cellwise outliers
Fuzzy clustering is a technique for identifying subgroups in heterogeneous populations by quantifying unit membership degrees. The magnitude of the latter depends on the desired level of fuzzification, based on the purpose of the analysis. We combine the advantages of fuzzy clustering with a robust approach able to detecting cellwise outliers, i.e., anomalous cells in a data matrix. The proposed methodology is formulated within a probabilistic framework and estimated via an Expectation-Maximiza…

IOCC: Aligning Semantic and Cluster Centers for Few-shot Short Text Clustering
Jixuan Yin, Zhihao Yao, Wenshuai Huo, Xinmiao Yu, Xiaocheng Feng, Bo Li
https://arxiv.org/abs/2508.06126

IOCC: Aligning Semantic and Cluster Centers for Few-shot Short Text Clustering
In clustering tasks, it is essential to structure the feature space into clear, well-separated distributions. However, because short text representations have limited expressiveness, conventional methods struggle to identify cluster centers that truly capture each category's underlying semantics, causing the representations to be optimized in suboptimal directions. To address this issue, we propose IOCC, a novel few-shot contrastive learning method that achieves alignment between the cluster ce…