Tootfinder

This https://arxiv.org/abs/2305.18436 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
$K$-means clustering is a widely used machine learning method for identifying patterns in large datasets. Semidefinite programming (SDP) relaxations have recently been proposed for solving the $K$-means optimization problem that enjoy strong statistical optimality guarantees, but the prohibitive cost of implementing an SDP solver renders these guarantees inaccessible to practical datasets. By contrast, nonnegative matrix factorization (NMF) is a simple clustering algorithm that is widely used b…

This https://arxiv.org/abs/2110.02898 has been replaced.
link: https://scholar.google.com/scholar?q=a

Coresets for Kernel Clustering
We devise coresets for kernel $k$-Means with a general kernel, and use them to obtain new, more efficient, algorithms. Kernel $k$-Means has superior clustering capability compared to classical $k$-Means, particularly when clusters are non-linearly separable, but it also introduces significant computational challenges. We address this computational issue by constructing a coreset, which is a reduced dataset that accurately preserves the clustering costs. Our main result is a coreset for kernel…

Physics-based deep learning reveals rising heating demand heightens air pollution in Norwegian cities
Cong Cao, Ramit Debnath, R. Michael Alvarez
https://arxiv.org/abs/2405.04716 …

Physics-based deep learning reveals rising heating demand heightens air pollution in Norwegian cities
Policymakers frequently analyze air quality and climate change in isolation, disregarding their interactions. This study explores the influence of specific climate factors on air quality by contrasting a regression model with K-Means Clustering, Hierarchical Clustering, and Random Forest techniques. We employ Physics-based Deep Learning (PBDL) and Long Short-Term Memory (LSTM) to examine the air pollution predictions. Our analysis utilizes ten years (2009-2018) of daily traffic, weather, and ai…

Causal K-Means Clustering
Kwangho Kim, Jisu Kim, Edward H. Kennedy
https://arxiv.org/abs/2405.03083 https://arxiv.org/pdf/2405.03083<…

Causal K-Means Clustering
Causal effects are often characterized with population summaries. These might provide an incomplete picture when there are heterogeneous treatment effects across subgroups. Since the subgroup structure is typically unknown, it is more challenging to identify and evaluate subgroup effects than population effects. We propose a new solution to this problem: Causal k-Means Clustering, which harnesses the widely-used k-means clustering algorithm to uncover the unknown subgroup structure. Our problem…

Imbalanced Data Clustering using Equilibrium K-Means
Yudong He
https://arxiv.org/abs/2402.14490 https://arxiv.org/pdf/2402.14490

Imbalanced Data Clustering using Equilibrium K-Means
Imbalanced data, characterized by an unequal distribution of data points across different clusters, poses a challenge for traditional hard and fuzzy clustering algorithms, such as hard K-means (HKM, or Lloyd's algorithm) and fuzzy K-means (FKM, or Bezdek's algorithm). This paper introduces equilibrium K-means (EKM), a novel and simple K-means-type algorithm that alternates between just two steps, yielding significantly improved clustering results for imbalanced data by reducing the tendency of …

Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks
Botao Zhu, Ebrahim Bedeer, Ha H. Nguyen, Robert Barton, Jerome Henry
https://arxiv.org/abs/2403.15700

Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks
Energy load balancing is an essential issue in designing wireless sensor networks (WSNs). Clustering techniques are utilized as energy-efficient methods to balance the network energy and prolong its lifetime. In this paper, we propose an improved soft-k-means (IS-k-means) clustering algorithm to balance the energy consumption of nodes in WSNs. First, we use the idea of ``clustering by fast search and find of density peaks'' (CFSFDP) and kernel density estimation (KDE) to improve the selection o…

Sensitivity Sampling for $k$-Means: Worst Case and Stability Optimal Coreset Bounds
Nikhil Bansal, Vincent Cohen-Addad, Milind Prabhu, David Saulpic, Chris Schwiegelshohn
https://arxiv.org/abs/2405.01339

Sensitivity Sampling for $k$-Means: Worst Case and Stability Optimal Coreset Bounds
Coresets are arguably the most popular compression paradigm for center-based clustering objectives such as $k$-means. Given a point set $P$, a coreset $Ω$ is a small, weighted summary that preserves the cost of all candidate solutions $S$ up to a $(1\pm \varepsilon)$ factor. For $k$-means in $d$-dimensional Euclidean space the cost for solution $S$ is $\sum_{p\in P}\min_{s\in S}\|p-s\|^2$. A very popular method for coreset construction, both in theory and practice, is Sensitivity S…

This https://arxiv.org/abs/2304.06145 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMS_…

R-Shiny Applications for Local Clustering to be Included in the growclusters for R Package
growclusters for R is a package that estimates a partition structure for multivariate data. It does this by implementing a hierarchical version of k-means clustering that accounts for possible known dependencies in a collection of datasets, where each set draws its cluster means from a single, global partition. Each component data set in the collection corresponds to a known group in the data. This paper focuses on R Shiny applications that implement the clustering methodology and simulate data…

Behavior Generation with Latent Actions
Seungjae Lee, Yibin Wang, Haritheja Etukuru, H. Jin Kim, Nur Muhammad Mahi Shafiullah, Lerrel Pinto
https://arxiv.org/abs/2403.03181

Behavior Generation with Latent Actions
Generative modeling of complex behaviors from labeled datasets has been a longstanding problem in decision making. Unlike language or image generation, decision making requires modeling actions - continuous-valued vectors that are multimodal in their distribution, potentially drawn from uncurated sources, where generation errors can compound in sequential prediction. A recent class of models called Behavior Transformers (BeT) addresses this by discretizing actions using k-means clustering to ca…

Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks
Botao Zhu, Ebrahim Bedeer, Ha H. Nguyen, Robert Barton, Jerome Henry
https://arxiv.org/abs/2403.15700

Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks
Energy load balancing is an essential issue in designing wireless sensor networks (WSNs). Clustering techniques are utilized as energy-efficient methods to balance the network energy and prolong its lifetime. In this paper, we propose an improved soft-k-means (IS-k-means) clustering algorithm to balance the energy consumption of nodes in WSNs. First, we use the idea of ``clustering by fast search and find of density peaks'' (CFSFDP) and kernel density estimation (KDE) to improve the selection o…

This https://arxiv.org/abs/2209.03597 has been replaced.
link: https://scholar.google.com/scholar?q=a

A penalized criterion for selecting the number of clusters for K-medians
Clustering is a usual unsupervised machine learning technique for grouping the data points into groups based upon similar features. We focus here on unsupervised clustering for contaminated data, i.e in the case where K-medians should be preferred to K-means because of its robustness. More precisely, we concentrate on a common question in clustering: how to chose the number of clusters? The answer proposed here is to consider the choice of the optimal number of clusters as the minimization of a…

Data Nuggets: A Method for Reducing Big Data While Preserving Data Structure
Traymon E. Beavers, Ge Cheng, Yajie Duan, Javier Cabrera, Mariusz Lubomirski, Dhammika Amaratunga, Jeffrey E. Teigler
https://arxiv.org/abs/2403.03099

Data Nuggets: A Method for Reducing Big Data While Preserving Data Structure
Big data, with NxP dimension where N is extremely large, has created new challenges for data analysis, particularly in the realm of creating meaningful clusters of data. Clustering techniques, such as K-means or hierarchical clustering are popular methods for performing exploratory analysis on large datasets. Unfortunately, these methods are not always possible to apply to big data due to memory or time constraints generated by calculations of order PxN(N-1). To circumvent this problem, typical…

A Comprehensive System for Secondary Structure Analysis of Protein Models
Vedh Kannan
https://arxiv.org/abs/2404.02909 https://arxiv.…

A Comprehensive System for Secondary Structure Analysis of Protein Models
In protein structure analysis, the accurate characterization of secondary structure elements is crucial for understanding protein function and dynamics. This paper presents a software system designed for the comprehensive analysis of the secondary structure of protein models. Leveraging phi (ϕ) and psi (ψ) torsion angles, the system utilises K-means clustering and outlier detection techniques to identify and classify folding structures within protein models. Through the visualisation of the R…

Superior Parallel Big Data Clustering through Competitive Stochastic Sample Size Optimization in Big-means
Rustam Mussabayev, Ravil Mussabayev
https://arxiv.org/abs/2403.18766

Superior Parallel Big Data Clustering through Competitive Stochastic Sample Size Optimization in Big-means
This paper introduces a novel K-means clustering algorithm, an advancement on the conventional Big-means methodology. The proposed method efficiently integrates parallel processing, stochastic sampling, and competitive optimization to create a scalable variant designed for big data applications. It addresses scalability and computation time challenges typically faced with traditional techniques. The algorithm adjusts sample sizes dynamically for each worker during execution, optimizing performa…

This https://arxiv.org/abs/2404.01085 has been replaced.
link: https://scholar.google.com/scholar?q=a

Analyzing Common Social and Physical Features of Flash-Flood Vulnerability Hotspots in Urban Areas
Flash flooding events, with their intense and sudden nature, present unique challenges for disaster researchers and emergency planners. To quantify the extent to which hotspots of flash flooding share similar social and physical features, the research uses community scale crowdsourced data and k means clustering. Crowdsourced data offers the potential to allocate limited resources, to improve spatial understanding, and to minimize the future effects of natural hazards. The research evaluates th…

Efficient Algorithms for Sum-of-Minimum Optimization
Lisang Ding, Ziang Chen, Xinshang Wang, Wotao Yin
https://arxiv.org/abs/2402.07070 https://

Efficient Algorithms for Sum-of-Minimum Optimization
In this work, we propose a novel optimization model termed ``sum-of-minimum" optimization. This model computes the sum (or average) of $N$ values where each is the minimum of the $k$ results of applying a distinct objective function to the same set of $k$ variables, and the model seeks to minimize this sum (or average) over those $k$ variables. When the $N$ functions are distance measures, we recover the $k$-means clustering problem by treating the $k$ variables as $k$ cluster centroids and the…

This https://arxiv.org/abs/2402.17714 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Cluster Scanning: a novel approach to resonance searches
We propose a new model-independent method for new physics searches called Cluster Scanning. It uses the k-means algorithm to perform clustering in the space of low-level event or jet observables, and separates potentially anomalous clusters to construct a signal-enriched region. The invariant mass spectra in these two regions are then used to determine whether a resonant signal is present. A pseudo-analysis on the LHC Olympics dataset with a $Z'$ resonance shows that Cluster Scanning outperform…

This https://arxiv.org/abs/2312.14916 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCC_…

Complexity of Local Search for Euclidean Clustering Problems
We show that the simplest local search heuristics for two natural Euclidean clustering problems are PLS-complete. First, we show that the Hartigan--Wong method for $k$-Means clustering is PLS-complete, even when $k = 2$. Second, we show the same result for the Flip heuristic for Max Cut, even when the edge weights are given by the (squared) Euclidean distances between the points in some set $\mathcal{X} \subseteq \mathbb{R}^d$; a problem which is equivalent to Min Sum 2-Clustering.

Clustering Analysis of US COVID-19 Rates, Vaccine Participation, and Socioeconomic Factors
Morteza Maleki
https://arxiv.org/abs/2404.08186 https://<…

Clustering Analysis of US COVID-19 Rates, Vaccine Participation, and Socioeconomic Factors
The COVID-19 pandemic has presented unprecedented challenges worldwide, with its impact varying significantly across different geographic and socioeconomic contexts. This study employs a clustering analysis to examine the diversity of responses to the pandemic within the United States, aiming to provide nuanced insights into the effectiveness of various strategies. We utilize an unsupervised machine learning approach, specifically K-Means clustering, to analyze county-level data that includes v…

This https://arxiv.org/abs/2305.18436 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
$K$-means clustering is a widely used machine learning method for identifying patterns in large datasets. Semidefinite programming (SDP) relaxations have recently been proposed for solving the $K$-means optimization problem that enjoy strong statistical optimality guarantees, but the prohibitive cost of implementing an SDP solver renders these guarantees inaccessible to practical datasets. By contrast, nonnegative matrix factorization (NMF) is a simple clustering algorithm that is widely used b…

Integrating Preprocessing Methods and Convolutional Neural Networks for Effective Tumor Detection in Medical Imaging
Ha Anh Vu
https://arxiv.org/abs/2402.16221

Integrating Preprocessing Methods and Convolutional Neural Networks for Effective Tumor Detection in Medical Imaging
This research presents a machine-learning approach for tumor detection in medical images using convolutional neural networks (CNNs). The study focuses on preprocessing techniques to enhance image features relevant to tumor detection, followed by developing and training a CNN model for accurate classification. Various image processing techniques, including Gaussian smoothing, bilateral filtering, and K-means clustering, are employed to preprocess the input images and highlight tumor regions. The…

This https://arxiv.org/abs/2305.18436 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
$K$-means clustering is a widely used machine learning method for identifying patterns in large datasets. Semidefinite programming (SDP) relaxations have recently been proposed for solving the $K$-means optimization problem that enjoy strong statistical optimality guarantees, but the prohibitive cost of implementing an SDP solver renders these guarantees inaccessible to practical datasets. By contrast, nonnegative matrix factorization (NMF) is a simple clustering algorithm that is widely used b…

This https://arxiv.org/abs/2211.15118 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Faster $k$-means++ Algorithm
$k$-means++ is an important algorithm for choosing initial cluster centers for the $k$-means clustering algorithm. In this work, we present a new algorithm that can solve the $k$-means++ problem with nearly optimal running time. Given $n$ data points in $\mathbb{R}^d$, the current state-of-the-art algorithm runs in $\widetilde{O}(k )$ iterations, and each iteration takes $\widetilde{O}(nd k)$ time. The overall running time is thus $\widetilde{O}(n d k^2)$. We propose a new algorithm \textsc{Fas…

Cluster Scanning: a novel approach to resonance searches
Ivan Oleksiyuk, John Andrew Raine, Michael Kr\"amer, Svyatoslav Voloshynovskiy, Tobias Golling
https://arxiv.org/abs/2402.17714

Cluster Scanning: a novel approach to resonance searches
We propose a new model-independent method for new physics searches called Cluster Scanning. It uses the k-means algorithm to perform clustering in the space of low-level event or jet observables, and separates potentially anomalous clusters to construct a signal-enriched region. The invariant mass spectra in these two regions are then used to determine whether a resonant signal is present. A pseudo-analysis on the LHC Olympics dataset with a $Z'$ resonance shows that Cluster Scanning outperform…

This https://arxiv.org/abs/2201.00087 has been replaced.
link: https://scholar.google.com/scholar?q=a

Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest
We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the…

A Scalable Algorithm for Individually Fair K-means Clustering
MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi
https://arxiv.org/abs/2402.06730

A Scalable Algorithm for Individually Fair K-means Clustering
We present a scalable algorithm for the individually fair ($p$, $k$)-clustering problem introduced by Jung et al. and Mahabadi et al. Given $n$ points $P$ in a metric space, let $δ(x)$ for $x\in P$ be the radius of the smallest ball around $x$ containing at least $n / k$ points. A clustering is then called individually fair if it has centers within distance $δ(x)$ of $x$ for each $x\in P$. While good approximation algorithms are known for this problem no efficient practical algorithms with go…

Cluster analysis of the Roma-BZCAT blazars
D. O. Kudryavtsev, Yu. V. Sotnikova, V. A. Stolyarov, T. V. Mufakharov, V. V. Vlasyuk, M. L. Khabibullina, A. G. Mikhailov, Yu. V. Cherepkova
https://arxiv.org/abs/2404.09667

Cluster analysis of the Roma-BZCAT blazars
Based on the collected multiwavelength data, namely in the radio (NVSS, FIRST, RATAN-600), IR (WISE), optical (Pan-STARRS), UV (GALEX), and X-ray (ROSAT, Swift-XRT) ranges, we have performed a cluster analysis for the blazars of the Roma-BZCAT catalog. Using two machine learning methods, namely a combination of PCA with k-means clustering and Kohonen's self-organizing maps, we have constructed an independent classification of the blazars (five classes) and compared the classes with the known Ro…

A Scalable Algorithm for Individually Fair K-means Clustering
MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi
https://arxiv.org/abs/2402.06730

A Scalable Algorithm for Individually Fair K-means Clustering
We present a scalable algorithm for the individually fair ($p$, $k$)-clustering problem introduced by Jung et al. and Mahabadi et al. Given $n$ points $P$ in a metric space, let $δ(x)$ for $x\in P$ be the radius of the smallest ball around $x$ containing at least $n / k$ points. A clustering is then called individually fair if it has centers within distance $δ(x)$ of $x$ for each $x\in P$. While good approximation algorithms are known for this problem no efficient practical algorithms with go…

Exploring Cluster Analysis in Nelore Cattle Visual Score Attribution
Alexandre de Oliveira Bezerra, Rodrigo Goncalves Mateus, Vanessa Ap. de Moraes Weber, Fabricio de Lima Weber, Yasmin Alves de Arruda, Rodrigo da Costa Gomes, Gabriel Toshio Hirokawa Higa, Hemerson Pistori
https://arxiv.org/abs/2403.07137

Exploring Cluster Analysis in Nelore Cattle Visual Score Attribution
Assessing the biotype of cattle through human visual inspection is a very common and important practice in precision cattle breeding. This paper presents the results of a correlation analysis between scores produced by humans for Nelore cattle and a variety of measurements that can be derived from images or other instruments. It also presents a study using the k-means algorithm to generate new ways of clustering a batch of cattle using the measurements that most correlate with the animal's body…